markdown
stringlengths 0
1.02M
| code
stringlengths 0
832k
| output
stringlengths 0
1.02M
| license
stringlengths 3
36
| path
stringlengths 6
265
| repo_name
stringlengths 6
127
|
|---|---|---|---|---|---|
3. (3 pts) Generate a series of four 3D scatter plots at selected time points to visually convey what is going on. Arrange the plots in a single row from left to right. Make sure you indicate which time points you are showing.
|
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
lim = 70
plt.figure(figsize=(12,3))
for (i,t) in enumerate([0, 100, 1000, 1999]):
ax = plt.subplot(1, 4, i+1, projection='3d')
x = positions[:,0,t]
y = positions[:,1,t]
z = positions[:,2,t]
ax.scatter(x, y, z)
plt.xlim([-lim, lim])
plt.ylim([-lim, lim])
ax.set_zlim([-lim, lim])
plt.xlabel("x")
plt.ylabel("y")
ax.set_zlabel("z")
plt.title(f"Time {t}");
|
_____no_output_____
|
Unlicense
|
homework/key-random_walks.ipynb
|
nishadalal120/NEU-365P-385L-Spring-2021
|
4. (3 pts) Draw the path of a single particle (your choice) across all time steps in a 3D plot.
|
ax = plt.subplot(1, 1, 1, projection='3d')
i = 10 # particle index
x = positions[i,0,:]
y = positions[i,1,:]
z = positions[i,2,:]
plt.plot(x, y, z)
plt.xlabel("x")
plt.ylabel("y")
ax.set_zlabel("z")
plt.title(f"Particle {i}");
|
_____no_output_____
|
Unlicense
|
homework/key-random_walks.ipynb
|
nishadalal120/NEU-365P-385L-Spring-2021
|
5. (3 pts) Find the minimum, maximum, mean and variance for the jump distances of all particles throughout the entire simulation. Jump distance is the euclidean distance moved on each time step $\sqrt(dx^2+dy^2+dz^2)$. *Hint: numpy makes this very simple.*
|
jumpsXYZForAllParticlesAndAllTimeSteps = positions[:,:,1:] - positions[:,:,:-1]
jumpDistancesForAllParticlesAndAllTimeSteps = np.sqrt(np.sum(jumpsXYZForAllParticlesAndAllTimeSteps**2, axis=1))
print(f"min = {jumpDistancesForAllParticlesAndAllTimeSteps.min()}")
print(f"max = {jumpDistancesForAllParticlesAndAllTimeSteps.max()}")
print(f"mean = {jumpDistancesForAllParticlesAndAllTimeSteps.mean()}")
print(f"var = {jumpDistancesForAllParticlesAndAllTimeSteps.var()}")
|
min = 0.0052364433932233926
max = 1.7230154410954457
mean = 0.9602742572616196
var = 0.07749699927626445
|
Unlicense
|
homework/key-random_walks.ipynb
|
nishadalal120/NEU-365P-385L-Spring-2021
|
6. (3 pts) Repeat the simulation, but this time confine the particles to a unit cell of dimension 10x10x10. Make it so that if a particle leaves one edge of the cell, it enters on the opposite edge (this is the sort of thing most molecular dynamics simulations do). Show plots as in 3 to visualize the simulation (note that most interesting stuff liekly happens in the first 100 time steps).
|
for t in range(numTimeSteps-1):
# 2 * [0 to 1] - 1 --> [-1 to 1]
jumpsForAllParticles = 2 * np.random.random((numParticles, 3)) - 1
positions[:,:,t+1] = positions[:,:,t] + jumpsForAllParticles
# check for out-of-bounds and warp to opposite bound
for i in range(numParticles):
for j in range(3):
if positions[i,j,t+1] < 0:
positions[i,j,t+1] += 10
elif positions[i,j,t+1] > 10:
positions[i,j,t+1] -= 10
plt.figure(figsize=(12,3))
for (i,t) in enumerate([0, 3, 10, 1999]):
ax = plt.subplot(1, 4, i+1, projection='3d')
x = positions[:,0,t]
y = positions[:,1,t]
z = positions[:,2,t]
ax.scatter(x, y, z)
plt.xlim([0, 10])
plt.ylim([0, 10])
ax.set_zlim([0, 10])
plt.xlabel("x")
plt.ylabel("y")
ax.set_zlabel("z")
plt.title(f"Time {t}");
|
_____no_output_____
|
Unlicense
|
homework/key-random_walks.ipynb
|
nishadalal120/NEU-365P-385L-Spring-2021
|
- 使用ngram进行恶意域名识别- 参考论文：https://www.researchgate.net/publication/330843380_Malicious_Domain_Names_Detection_Algorithm_Based_on_N_-Gram
|
import numpy as np
import pandas as pd
import tldextract
import matplotlib.pyplot as plt
import os
import re
import time
from scipy import sparse
%matplotlib inline
|
_____no_output_____
|
MIT
|
malicious_domain_detect.ipynb
|
aierwiki/ngram-detection
|
加载数据 - 加载正常域名
|
df_benign_domain = pd.read_csv('top-1m.csv', index_col=0, header=None).reset_index(drop=True)
df_benign_domain.columns = ['domain']
df_benign_domain['label'] = 0
|
_____no_output_____
|
MIT
|
malicious_domain_detect.ipynb
|
aierwiki/ngram-detection
|
- 加载恶意域名
|
df_malicious_domain = pd.read_csv('malicious-domain.csv', engine='python', header=None)
df_malicious_domain = df_malicious_domain[[1]]
df_malicious_domain.columns = ['domain']
df_malicious_domain = df_malicious_domain[df_malicious_domain['domain'] != '-']
df_malicious_domain['label'] = 1
df_domain = pd.concat([df_benign_domain, df_malicious_domain], axis=0)
def remove_tld(domain):
ext = tldextract.extract(domain)
if ext.subdomain != '':
domain = ext.subdomain + '.' + ext.domain
else:
domain = ext.domain
return domain
df_domain['domain'] = df_domain['domain'].map(lambda x: tldextract.extract(x).domain)
|
_____no_output_____
|
MIT
|
malicious_domain_detect.ipynb
|
aierwiki/ngram-detection
|
提取ngram特征
|
from sklearn.feature_extraction.text import CountVectorizer
domain_list = df_domain[df_domain['label'] == 0]['domain'].values.tolist()
benign_text_str = '.'.join(domain_list)
benign_text = re.split(r'[.-]', benign_text_str)
benign_text = list(filter(lambda x: len(x) >= 3, benign_text))
def get_ngram_weight_dict(benign_text):
cv = CountVectorizer(ngram_range = (3, 7), analyzer='char', max_features=100000)
cv.fit(benign_text)
feature_names = cv.get_feature_names()
benign_text_vectors = cv.transform(benign_text)
ngram_count = benign_text_vectors.sum(axis=0)
window_sizes = np.array(list(map(lambda x: len(x), feature_names)))
ngram_weights = np.multiply(np.log2(ngram_count), window_sizes)
ngram_weights = sparse.csr_matrix(ngram_weights)
feature_names = cv.get_feature_names()
ngram_weights_dict = dict()
for ngram, weight in zip(feature_names, ngram_weights.toarray()[0].tolist()):
ngram_weights_dict[ngram] = weight
return ngram_weights_dict
ngram_weights_dict = get_ngram_weight_dict(benign_text)
|
_____no_output_____
|
MIT
|
malicious_domain_detect.ipynb
|
aierwiki/ngram-detection
|
计算域名的信誉值
|
def get_reputation_value(ngram_weights_dict, domain):
if len(domain) < 3:
return 1000
domains = re.split(r'[.-]', domain)
reputation = 0
domain_len = 0
for domain in domains:
domain_len += len(domain)
for window_size in range(3, 8):
for i in range(len(domain) - window_size + 1):
reputation += ngram_weights_dict.get(domain[i:i+window_size], 0)
reputation = reputation / domain_len
return reputation
get_reputation_value(ngram_weights_dict, 'google')
get_reputation_value(ngram_weights_dict, 'ta0ba0')
get_reputation_value(ngram_weights_dict, 'dskdjisuowerwdfskdfj000')
start = time.time()
df_domain['reputation'] = df_domain['domain'].map(lambda x: get_reputation_value(ngram_weights_dict, x))
end = time.time()
print('cost time : {}'.format(end - start))
df_domain[df_domain['label'] == 0]['reputation'].describe()
df_domain[df_domain['label'] == 1]['reputation'].describe()
|
_____no_output_____
|
MIT
|
malicious_domain_detect.ipynb
|
aierwiki/ngram-detection
|
保存模型文件
|
import joblib
joblib.dump(ngram_weights_dict, 'ngram_weights_dict.m', compress=4)
|
_____no_output_____
|
MIT
|
malicious_domain_detect.ipynb
|
aierwiki/ngram-detection
|
Another attempt at MC Simulation on AHP/ANP The ideas are the following:1. There is a class MCAnp that has a sim() method that will simulate any Prioritizer2. MCAnp also has a sim_fill() function that does fills in the data needed for a single simulation Import needed libs
|
import pandas as pd
import sys
import os
sys.path.insert(0, os.path.abspath("../"))
import numpy as np
from scipy.stats import triang
from copy import deepcopy
from pyanp.priority import pri_eigen
from pyanp.pairwise import Pairwise
from pyanp.ahptree import AHPTree, AHPTreeNode
from pyanp.direct import Direct
|
_____no_output_____
|
MIT
|
scrap/MCAnpResearch.ipynb
|
georg-cantor/pyanp
|
MCAnp class
|
def ascale_mscale(val:(float,int))->float:
if val is None:
return 0
elif val < 0:
val = -val
val += 1
val = 1.0/val
return val
else:
return val+1
def mscale_ascale(val:(float,int))->float:
if val == 0:
return None
elif val >= 1:
return val - 1
else:
val = 1/val
val = val-1
return -val
DEFAULT_DISTRIB = triang(c=0.5, loc=-1.5, scale=3.0)
def avote_random(avote):
"""
Returns a random additive vote in the neighborhood of the additive vote avote
according to the default disribution DEFAULT_DISTRIB
"""
if avote is None:
return None
raw_val = DEFAULT_DISTRIB.rvs(size=1)[0]
return avote+raw_val
def mvote_random(mvote):
"""
Returns a random multiplicative vote in the neighborhhod of the multiplicative vote mvote
according to the default distribution DEFAULT_DISTRIB. This is handled by converting
the multiplicative vote to an additive vote, calling avote_random() and converting the
result back to an additive vote
"""
avote = mscale_ascale(mvote)
rval_a = avote_random(avote)
rval = ascale_mscale(rval_a)
return rval
def direct_random(direct, max_percent_chg=0.2)->float:
"""
Returns a random direct data value near the value `direct'. This function
creates a random percent change, between -max_percent_chg and +max_percent_chg, and
then changes the direct value by that factor, and returns it.
"""
pchg = np.random.uniform(low=-max_percent_chg, high=max_percent_chg)
return direct * (1 + pchg)
class MCAnp:
def __init__(self):
# Setup the random pairwise vote generator
self.pwvote_random = mvote_random
# Setup the random direct vote generator
self.directvote_random = direct_random
# Set the default user to use across the simulation
# follows the standard from Pairwise class, i.e. it can be a list
# of usernames, a single username, or None (which means total group average)
self.username = None
# What is the pairwise priority calculation?
self.pwprioritycalc = pri_eigen
def sim_fill(self, src, dest):
"""
Fills in data on a structure prior to doing the simulation calculations.
This function calls sim_NAME_fill depending on the class of the src object.
If the dest object is None, we create a dest object by calling deepcopy().
In either case, we always return the allocated dest object
"""
if dest is None:
dest = deepcopy(src)
# Which kind of src do we have
if isinstance(src, np.ndarray):
# We are simulating on a pairwise comparison matrix
return self.sim_pwmat_fill(src, dest)
elif isinstance(src, Pairwise):
# We are simulating on a multi-user pairwise comparison object
return self.sim_pw_fill(src, dest)
elif isinstance(src, AHPTree):
# We are simulating on an ahp tree object
return self.sim_ahptree_fill(src, dest)
elif isinstance(src, Direct):
# We are simulating on an ahp direct data
return self.sim_direct_fill(src, dest)
else:
raise ValueError("Src class is not handled, it is "+type(src).__name__)
def sim_pwmat_fill(self, pwsrc:np.ndarray, pwdest:np.ndarray=None)->np.ndarray:
"""
Fills in a pairwise comparison matrix with noisy votes based on pwsrc
If pwsrc is None, we create a new matrix, otherwise we fill in pwdest
with noisy values based on pwsrc and the self.pwvote_random parameter.
In either case, we return the resulting noisy matrix
"""
if pwdest is None:
pwdest = deepcopy(pwsrc)
size = len(pwsrc)
for row in range(size):
pwdest[row,row] = 1.0
for col in range(row+1, size):
val = pwsrc[row,col]
if val >= 1:
nvote = self.pwvote_random(val)
pwdest[row, col]=nvote
pwdest[col, row]=1/nvote
elif val!= 0:
nvote = self.pwvote_random(1/val)
pwdest[col, row] = nvote
pwdest[row, col] = 1/nvote
else:
pwdest[row, col] = nvote
pwdest[col, row] = nvote
return pwdest
def sim_pwmat(self, pwsrc:np.ndarray, pwdest:np.ndarray=None)->np.ndarray:
"""
creates a noisy pw comparison matrix from pwsrc, stores the matrix in pwdest (which
is created if pwdest is None) calculates the resulting priority and returns that
"""
pwdest = self.sim_pwmat_fill(pwsrc, pwdest)
rval = self.pwprioritycalc(pwdest)
return rval
def sim_pw(self, pwsrc:Pairwise, pwdest:Pairwise)->np.ndarray:
"""
Performs a simulation on a pairwise comparison matrix object and returns the
resulting priorities
"""
pwdest = self.sim_pw_fill(pwsrc, pwdest)
mat = pwdest.matrix(self.username)
rval = self.pwprioritycalc(mat)
return rval
def sim_pw_fill(self, pwsrc:Pairwise, pwdest:Pairwise=None)->Pairwise:
"""
Fills in the pairwise comparison structure of pwdest with noisy pairwise data from pwsrc.
If pwdest is None, we create one first, then fill in. In either case, we return the pwdest
object with new noisy data in it.
"""
if pwdest is None:
pwdest = deepcopy(pwsrc)
for user in pwsrc.usernames():
srcmat = pwsrc.matrix(user)
destmat = pwdest.matrix(user)
self.sim_pwmat_fill(srcmat, destmat)
return pwdest
def sim_direct_fill(self, directsrc:Direct, directdest:Direct=None)->Direct:
"""
Fills in the direct data structure of directdest with noisy data from directsrc.
If directdest is None, we create on as a deep copy of directsrc, then fill in.
In either case, we return the directdest object with new noisy data in it.
"""
if directdest is None:
directdest = deepcopy(directsrc)
for altpos in range(len(directdest)):
orig = directsrc[altpos]
newvote = self.directvote_random(orig)
directdest.data[altpos] = newvote
return directdest
def sim_direct(self, directsrc:Direct, directdest:Direct=None)->np.ndarray:
"""
Simulates for direct data
"""
directdest = self.sim_direct_fill(directsrc, directdest)
return directdest.priority()
def sim_ahptree_fill(self, ahpsrc:AHPTree, ahpdest:AHPTree)->AHPTree:
"""
Fills in the ahp tree structure of ahpdest with noisy data from ahpsrc.
If ahpdest is None, we create one as a deepcopy of ahpsrc, then fill in.
In either case, we return the ahpdest object with new noisy data in it.
"""
if ahpdest is None:
ahpdest = deepcopy(ahpsrc)
self.sim_ahptreenode_fill(ahpsrc.root, ahpdest.root)
return ahpdest
def sim_ahptreenode_fill(self, nodesrc:AHPTreeNode, nodedest:AHPTreeNode)->AHPTreeNode:
"""
Fills in data in an AHPTree
"""
#Okay, first we fill in for the alt_prioritizer
if nodesrc.alt_prioritizer is not None:
self.sim_fill(nodesrc.alt_prioritizer, nodedest.alt_prioritizer)
#Now wefill in the child prioritizer
if nodesrc.child_prioritizer is not None:
self.sim_fill(nodesrc.child_prioritizer, nodedest.child_prioritizer)
#Now for each child, fill in
for childsrc, childdest in zip(nodesrc.children, nodedest.children):
self.sim_ahptreenode_fill(childsrc, childdest)
#We are done, return the dest
return nodedest
def sim_ahptree(self, ahpsrc:AHPTree, ahpdest:AHPTree)->np.ndarray:
"""
Perform the actual simulation
"""
ahpdest = self.sim_ahptree_fill(ahpsrc, ahpdest)
return ahpdest.priority()
mc = MCAnp()
pw = np.array([
[1, 1/2, 3],
[2, 1, 5],
[1/3, 1/5, 1]
])
rpw= mc.sim_pwmat_fill(pw)
rpw
[mc.sim_pwmat(pw) for i in range(20)]
pwobj = Pairwise(alts=['alt '+str(i) for i in range(3)])
pwobj.vote_matrix(user_name='u1', val=pw)
|
_____no_output_____
|
MIT
|
scrap/MCAnpResearch.ipynb
|
georg-cantor/pyanp
|
Checking that the deep copy is actually a deep copyFor some reason deepcopy was not copying the matrix, I had to overwrite__deepcopy__ in Pairwise
|
pwobj.matrix('u1')
rpwobj = pwobj.__deepcopy__()
a=rpwobj
b=pwobj
a.df
display(a.df.loc['u1', 'Matrix'])
display(b.df.loc['u1', 'Matrix'])
display(a.matrix('u1') is b.matrix('u1'))
display(a.matrix('u1') == b.matrix('u1'))
|
_____no_output_____
|
MIT
|
scrap/MCAnpResearch.ipynb
|
georg-cantor/pyanp
|
Now let's try to simulate
|
[mc.sim_pw(pwobj, rpwobj) for i in range(20)]
pwobj.matrix('u1')
|
_____no_output_____
|
MIT
|
scrap/MCAnpResearch.ipynb
|
georg-cantor/pyanp
|
Try to simulate a direct data
|
dd = Direct(alt_names=['a1', 'a2', 'a3'])
dd.data[0]=0.5
dd.data[1]=0.3
dd.data[2]=0.2
rdd=mc.sim_direct_fill(dd)
rdd.data
|
_____no_output_____
|
MIT
|
scrap/MCAnpResearch.ipynb
|
georg-cantor/pyanp
|
Simulate an ahptree
|
alts=['alt '+str(i) for i in range(3)]
tree = AHPTree(alt_names=alts)
kids = ['crit '+str(i) for i in range(4)]
for kid in kids:
tree.add_child(kid)
node = tree.get_node(kid)
direct = node.alt_prioritizer
s = 0
for alt in alts:
direct[alt] = np.random.uniform()
s += direct[alt]
if s != 0:
for alt in alts:
direct[alt] /= s
tree.priority()
mc.sim_ahptree(tree, None)
tree.priority()
|
_____no_output_____
|
MIT
|
scrap/MCAnpResearch.ipynb
|
georg-cantor/pyanp
|
Laboratorio 5 Datos: _European Union lesbian, gay, bisexual and transgender survey (2012)_Link a los datos [aquí](https://www.kaggle.com/ruslankl/european-union-lgbt-survey-2012). ContextoLa FRA (Agencia de Derechos Fundamentales) realizó una encuesta en línea para identificar cómo las personas lesbianas, gays, bisexuales y transgénero (LGBT) que viven en la Unión Europea y Croacia experimentan el cumplimiento de sus derechos fundamentales. La evidencia producida por la encuesta apoyará el desarrollo de leyes y políticas más efectivas para combatir la discriminación, la violencia y el acoso, mejorando la igualdad de trato en toda la sociedad. La necesidad de una encuesta de este tipo en toda la UE se hizo evidente después de la publicación en 2009 del primer informe de la FRA sobre la homofobia y la discriminación por motivos de orientación sexual o identidad de género, que destacó la ausencia de datos comparables. La Comisión Europea solicitó a FRA que recopilara datos comparables en toda la UE sobre este tema. FRA organizó la recopilación de datos en forma de una encuesta en línea que abarca todos los Estados miembros de la UE y Croacia. Los encuestados eran personas mayores de 18 años, que se identifican como lesbianas, homosexuales, bisexuales o transgénero, de forma anónima. La encuesta se hizo disponible en línea, de abril a julio de 2012, en los 23 idiomas oficiales de la UE (excepto irlandés) más catalán, croata, luxemburgués, ruso y turco. En total, 93,079 personas LGBT completaron la encuesta. Los expertos internos de FRA diseñaron la encuesta que fue implementada por Gallup, uno de los líderes del mercado en encuestas a gran escala. Además, organizaciones de la sociedad civil como ILGA-Europa (Región Europea de la Asociación Internacional de Lesbianas, Gays, Bisexuales, Trans e Intersexuales) y Transgender Europe (TGEU) brindaron asesoramiento sobre cómo acercarse mejor a las personas LGBT.Puede encontrar más información sobre la metodología de la encuesta en el [__Informe técnico de la encuesta LGBT de la UE. Metodología, encuesta en línea, cuestionario y muestra__](https://fra.europa.eu/sites/default/files/eu-lgbt-survey-technical-report_en.pdf). ContenidoEl conjunto de datos consta de 5 archivos .csv que representan 5 bloques de preguntas: vida cotidiana, discriminación, violencia y acoso, conciencia de los derechos, preguntas específicas de personas transgénero.El esquema de todas las tablas es idéntico:* `CountryCode` - name of the country* `subset` - Lesbian, Gay, Bisexual women, Bisexual men or Transgender (for Transgender Specific Questions table the value is only Transgender)* `question_code` - unique code ID for the question* `question_label` - full question text* `answer` - answer given* `percentage`* `notes` - [0]: small sample size; [1]: NA due to small sample size; [2]: missing valueEn el laboratorio de hoy solo utilizaremos los relacionados a la vida cotidiana, disponibles en el archivo `LGBT_Survey_DailyLife.csv` dentro de la carpeta `data`.
|
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
daily_life_raw = pd.read_csv(os.path.join("..", "data", "LGBT_Survey_DailyLife.csv"))
daily_life_raw.head()
daily_life_raw.info()
daily_life_raw.describe(include="all").T
questions = (
daily_life_raw.loc[: , ["question_code", "question_label"]]
.drop_duplicates()
.set_index("question_code")
.squeeze()
)
for idx, value in questions.items():
print(f"Question code {idx}:\n\n{value}\n\n")
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Preprocesamiento de datos ¿Te fijaste que la columna `percentage` no es numérica? Eso es por los registros con notes `[1]`, por lo que los eliminaremos.
|
daily_life_raw.notes.unique()
daily_life = (
daily_life_raw.query("notes != ' [1] '")
.astype({"percentage": "int"})
.drop(columns=["question_label", "notes"])
.rename(columns={"CountryCode": "country"})
)
daily_life.head()
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Ejercicio 1(1 pto)¿A qué tipo de dato (nominal, ordinal, discreto, continuo) corresponde cada columna del DataFrame `daily_life`?Recomendación, mira los valores únicos de cada columna.
|
daily_life.dtypes
# FREE STYLE #
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
__Respuesta:__* `country`: * `subset`: * `question_code`:* `answer`: * `percentage`: Ejercicio 2 (1 pto)Crea un nuevo dataframe `df1` tal que solo posea registros de Bélgica, la pregunta con código `b1_b` y que hayan respondido _Very widespread_.Ahora, crea un gráfico de barras vertical con la función `bar` de `matplotlib` para mostrar el porcentaje de respuestas por cada grupo. La figura debe ser de tamaño 10 x 6 y el color de las barras verde.
|
print(f"Question b1_b:\n\n{questions['b1_b']}")
df1 = # FIX ME #
df1
x = # FIX ME #
y = # FIX ME #
fig = plt.figure(# FIX ME #)
plt# FIX ME #
plt.show()
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Ejercicio 3(1 pto)Respecto a la pregunta con código `g5`, ¿Cuál es el porcentaje promedio por cada valor de la respuesta (notar que la respuestas a las preguntas son numéricas)?
|
print(f"Question g5:\n\n{questions['g5']}")
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Crea un DataFrame llamado `df2` tal que:1. Solo sean registros con la pregunta con código `g5`2. Cambia el tipo de la columna `answer` a `int`.3. Agrupa por país y respuesta y calcula el promedio a la columna porcentaje (usa `agg`).4. Resetea los índices.
|
df2 = (
# FIX ME #
)
df2
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Crea un DataFrame llamado `df2_mean` tal que:1. Agrupa `df2` por respuesta y calcula el promedio del porcentaje.2. Resetea los índices.
|
df2_mean = df2.# FIX ME #
df2_mean.head()
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Ahora, grafica lo siguiente:1. Una figura con dos columnas, tamaño de figura 15 x 12 y que compartan eje x y eje y. Usar `plt.subplots`.2. Para el primer _Axe_ (`ax1`), haz un _scatter plot_ tal que el eje x sea los valores de respuestas de `df2`, y el eye y corresponda a los porcentajes de `df2`. Recuerda que en este caso corresponde a promedios por país, por lo que habrán más de 10 puntos en el gráfico..3. Para el segundo _Axe_ (`ax2`), haz un gráfico de barras horizontal tal que el eje x sea los valores de respuestas de `df2_mean`, y el eye y corresponda a los porcentajes de `df2_mean`.
|
x = # FIX ME #
y = # FIX ME #
x_mean = # FIX ME #s
y_mean = # FIX ME #
fig, (ax1, ax2) = plt.subplots(# FIX ME #)
ax1.# FIX ME #
ax1.grid(alpha=0.3)
ax2.# FIX ME #
ax2.grid(alpha=0.3)
fig.show()
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Ejercicio 4(1 pto) Respecto a la misma pregunta `g5`, cómo se distribuyen los porcentajes en promedio para cada país - grupo?Utilizaremos el mapa de calor presentado en la clase, para ello es necesario procesar un poco los datos para conformar los elementos que se necesitan.Crea un DataFrame llamado `df3` tal que:1. Solo sean registros con la pregunta con código `g5`2. Cambia el tipo de la columna `answer` a `int`.3. Agrupa por país y subset, luego calcula el promedio a la columna porcentaje (usa `agg`).4. Resetea los índices.5. Pivotea tal que los índices sean los países, las columnas los grupos y los valores el promedio de porcentajes.6. Llena los valores nulos con cero. Usa `fillna`.
|
## Code from:
# https://matplotlib.org/3.1.1/gallery/images_contours_and_fields/image_annotated_heatmap.html#sphx-glr-gallery-images-contours-and-fields-image-annotated-heatmap-py
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
def heatmap(data, row_labels, col_labels, ax=None,
cbar_kw={}, cbarlabel="", **kwargs):
"""
Create a heatmap from a numpy array and two lists of labels.
Parameters
----------
data
A 2D numpy array of shape (N, M).
row_labels
A list or array of length N with the labels for the rows.
col_labels
A list or array of length M with the labels for the columns.
ax
A `matplotlib.axes.Axes` instance to which the heatmap is plotted. If
not provided, use current axes or create a new one. Optional.
cbar_kw
A dictionary with arguments to `matplotlib.Figure.colorbar`. Optional.
cbarlabel
The label for the colorbar. Optional.
**kwargs
All other arguments are forwarded to `imshow`.
"""
if not ax:
ax = plt.gca()
# Plot the heatmap
im = ax.imshow(data, **kwargs)
# Create colorbar
cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")
# We want to show all ticks...
ax.set_xticks(np.arange(data.shape[1]))
ax.set_yticks(np.arange(data.shape[0]))
# ... and label them with the respective list entries.
ax.set_xticklabels(col_labels)
ax.set_yticklabels(row_labels)
# Let the horizontal axes labeling appear on top.
ax.tick_params(top=True, bottom=False,
labeltop=True, labelbottom=False)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=-30, ha="right",
rotation_mode="anchor")
# Turn spines off and create white grid.
for edge, spine in ax.spines.items():
spine.set_visible(False)
ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
ax.tick_params(which="minor", bottom=False, left=False)
return im, cbar
def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
textcolors=["black", "white"],
threshold=None, **textkw):
"""
A function to annotate a heatmap.
Parameters
----------
im
The AxesImage to be labeled.
data
Data used to annotate. If None, the image's data is used. Optional.
valfmt
The format of the annotations inside the heatmap. This should either
use the string format method, e.g. "$ {x:.2f}", or be a
`matplotlib.ticker.Formatter`. Optional.
textcolors
A list or array of two color specifications. The first is used for
values below a threshold, the second for those above. Optional.
threshold
Value in data units according to which the colors from textcolors are
applied. If None (the default) uses the middle of the colormap as
separation. Optional.
**kwargs
All other arguments are forwarded to each call to `text` used to create
the text labels.
"""
if not isinstance(data, (list, np.ndarray)):
data = im.get_array()
# Normalize the threshold to the images color range.
if threshold is not None:
threshold = im.norm(threshold)
else:
threshold = im.norm(data.max())/2.
# Set default alignment to center, but allow it to be
# overwritten by textkw.
kw = dict(horizontalalignment="center",
verticalalignment="center")
kw.update(textkw)
# Get the formatter in case a string is supplied
if isinstance(valfmt, str):
valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)
# Loop over the data and create a `Text` for each "pixel".
# Change the text's color depending on the data.
texts = []
for i in range(data.shape[0]):
for j in range(data.shape[1]):
kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
texts.append(text)
return texts
df3 = (
# FIX ME #
)
df3.head()
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Finalmente, los ingredientes para el heat map son:
|
countries = df3.index.tolist()
subsets = df3.columns.tolist()
answers = df3.values
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
El mapa de calor debe ser de la siguiente manera:* Tamaño figura: 15 x 20* cmap = "YlGn"* cbarlabel = "Porcentaje promedio (%)"* Precición en las anotaciones: Flotante con dos decimales.
|
fig, ax = plt.subplots(# FIX ME #)
im, cbar = heatmap(# FIX ME #")
texts = annotate_heatmap(# FIX ME #)
fig.tight_layout()
plt.show()
|
_____no_output_____
|
BSD-3-Clause
|
labs/lab05.ipynb
|
aoguedao/mat281_2020S2
|
Talktorial 1 Compound data acquisition (ChEMBL) Developed in the CADD seminars 2017 and 2018, AG Volkamer, Charité/FU Berlin Paula Junge and Svetlana Leng Aim of this talktorialWe learn how to extract data from ChEMBL:* Find ligands which were tested on a certain target* Filter by available bioactivity data* Calculate pIC50 values* Merge dataframes and draw extracted molecules Learning goals Theory* ChEMBL database * ChEMBL web services * ChEMBL webresource client* Compound activity measures * IC50 * pIC50 Practical Goal: Get list of compounds with bioactivity data for a given target* Connect to ChEMBL database* Get target data (EGFR kinase)* Bioactivity data * Download and filter bioactivities * Clean and convert* Compound data * Get list of compounds * Prepare output data* Output * Draw molecules with highest pIC50 * Write output file References* ChEMBL bioactivity database (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210557/)* ChEMBL web services: Nucleic Acids Res. (2015), 43, 612-620 (https://academic.oup.com/nar/article/43/W1/W612/2467881) * ChEMBL webrescource client GitHub (https://github.com/chembl/chembl_webresource_client)* myChEMBL webservices version 2.x (https://github.com/chembl/mychembl/blob/master/ipython_notebooks/09_myChEMBL_web_services.ipynb)* ChEMBL web-interface (https://www.ebi.ac.uk/chembl/)* EBI-RDF platform (https://www.ncbi.nlm.nih.gov/pubmed/24413672)* IC50 and pIC50 (https://en.wikipedia.org/wiki/IC50)* UniProt website (https://www.uniprot.org/) _____________________________________________________________________________________________________________________ Theory ChEMBL database* Open large-scale bioactivity database* **Current data content (as of 10.2018):** * \>1.8 million distinct compound structures * \>15 million activity values from 1 million assays * Assays are mapped to ∼12 000 targets* **Data sources** include scientific literature, PubChem bioassays, Drugs for Neglected Diseases Initiative (DNDi), BindingDB database, ...* ChEMBL data can be accessed via a [web-interface](https://www.ebi.ac.uk/chembl/), the [EBI-RDF platform](https://www.ncbi.nlm.nih.gov/pubmed/24413672) and the [ChEMBL web services](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489243/B5) ChEMBL web services* RESTful web service* ChEMBL web service version 2.x resource schema: [](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4489243/figure/F2/)*Figure 1:* "ChEMBL web service schema diagram. The oval shapes represent ChEMBL web service resources and the line between two resources indicates that they share a common attribute. The arrow direction shows where the primary information about a resource type can be found. A dashed line indicates the relationship between two resources behaves differently. For example, the `Image` resource provides a graphical based representation of a `Molecule`."Figure and description taken from: [Nucleic Acids Res. (2015), 43, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881). ChEMBL webresource client* Python client library for accessing ChEMBL data* Handles interaction with the HTTPS protocol* Lazy evaluation of results -> reduced number of network requests Compound activity measures IC50 * [Half maximal inhibitory concentration](https://en.wikipedia.org/wiki/IC50)* Indicates how much of a particular drug or other substance is needed to inhibit a given biological process by half[](https://commons.wikimedia.org/wiki/File:Example_IC50_curve_demonstrating_visually_how_IC50_is_derived.png)*Figure 2:* Visual demonstration of how to derive an IC50 value: Arrange data with inhibition on vertical axis and log(concentration) on horizontal axis; then identify max and min inhibition; then the IC50 is the concentration at which the curve passes through the 50% inhibition level. pIC50* To facilitate the comparison of IC50 values, we define pIC50 values on a logarithmic scale, such that $ pIC_{50} = -log_{10}(IC_{50}) $ where $ IC_{50}$ is specified in units of M.* Higher pIC50 values indicate exponentially greater potency of the drug* pIC50 is given in terms of molar concentration (mol/L or M) * IC50 should be specified in M to convert to pIC50 * For nM: $pIC_{50} = -log_{10}(IC_{50}*10^{-9})= 9-log_{10}(IC_{50}) $ Besides, IC50 and pIC50, other bioactivity measures are used, such as the equilibrium constant [KI](https://en.wikipedia.org/wiki/Equilibrium_constant) and the half maximal effective concentration [EC50](https://en.wikipedia.org/wiki/EC50). PracticalIn the following, we want to download all molecules that have been tested against our target of interest, the EGFR kinase. Connect to ChEMBL database First, the ChEMBL webresource client as well as other python libraries are imported.
|
from chembl_webresource_client.new_client import new_client
import pandas as pd
import math
from rdkit.Chem import PandasTools
|
/home/andrea/anaconda2/envs/cadd-py36/lib/python3.6/site-packages/grequests.py:21: MonkeyPatchWarning: Monkey-patching ssl after ssl has already been imported may lead to errors, including RecursionError on Python 3.6. It may also silently lead to incorrect behaviour on Python 3.7. Please monkey-patch earlier. See https://github.com/gevent/gevent/issues/1016. Modules that had direct imports (NOT patched): ['urllib3.contrib.pyopenssl (/home/andrea/anaconda2/envs/cadd-py36/lib/python3.6/site-packages/urllib3/contrib/pyopenssl.py)', 'urllib3.util (/home/andrea/anaconda2/envs/cadd-py36/lib/python3.6/site-packages/urllib3/util/__init__.py)'].
curious_george.patch_all(thread=False, select=False)
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Create resource objects for API access.
|
targets = new_client.target
compounds = new_client.molecule
bioactivities = new_client.activity
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Target data* Get UniProt-ID (http://www.uniprot.org/uniprot/P00533) of the target of interest (EGFR kinase) from UniProt website (https://www.uniprot.org/)* Use UniProt-ID to get target information* Select a different UniProt-ID if you are interested into another target
|
uniprot_id = 'P00533'
# Get target information from ChEMBL but restrict to specified values only
target_P00533 = targets.get(target_components__accession=uniprot_id) \
.only('target_chembl_id', 'organism', 'pref_name', 'target_type')
print(type(target_P00533))
pd.DataFrame.from_records(target_P00533)
|
<class 'chembl_webresource_client.query_set.QuerySet'>
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
After checking the entries, we select the first entry as our target of interest`CHEMBL203`: It is a single protein and represents the human Epidermal growth factor receptor (EGFR, also named erbB1)
|
target = target_P00533[0]
target
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Save selected ChEMBL-ID.
|
chembl_id = target['target_chembl_id']
chembl_id
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Bioactivity dataNow, we want to query bioactivity data for the target of interest. Download and filter bioactivities for the target In this step, we download and filter the bioactivity data and only consider* human proteins* bioactivity type IC50* exact measurements (relation '=') * binding data (assay type 'B')
|
bioact = bioactivities.filter(target_chembl_id = chembl_id) \
.filter(type = 'IC50') \
.filter(relation = '=') \
.filter(assay_type = 'B') \
.only('activity_id','assay_chembl_id', 'assay_description', 'assay_type', \
'molecule_chembl_id', 'type', 'units', 'relation', 'value', \
'target_chembl_id', 'target_organism')
len(bioact), len(bioact[0]), type(bioact), type(bioact[0])
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
If you experience difficulties to query the ChEMBL database, we provide here a file containing the results for the query in the previous cell (11 April 2019). We do this using the Python package pickle which serializes Python objects so they can be saved to a file, and loaded in a program again later on.(Learn more about object serialization on [DataCamp](https://www.datacamp.com/community/tutorials/pickle-python-tutorial))You can load the "pickled" compounds by uncommenting and running the next cell.
|
#import pickle
#bioact = pickle.load(open("../data/T1/EGFR_compounds_from_chembl_query_20190411.p", "rb"))
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Clean and convert bioactivity dataThe data is stored as a list of dictionaries
|
bioact[0]
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Convert to pandas dataframe (this might take some minutes).
|
bioact_df = pd.DataFrame.from_records(bioact)
bioact_df.head(10)
bioact_df.shape
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Delete entries with missing values.
|
bioact_df = bioact_df.dropna(axis=0, how = 'any')
bioact_df.shape
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Delete duplicates:Sometimes the same molecule (`molecule_chembl_id`) has been tested more than once, in this case, we only keep the first one.
|
bioact_df = bioact_df.drop_duplicates('molecule_chembl_id', keep = 'first')
bioact_df.shape
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
We would like to only keep bioactivity data measured in molar units. The following print statements will help us to see what units are contained and to control what is kept after dropping some rows.
|
print(bioact_df.units.unique())
bioact_df = bioact_df.drop(bioact_df.index[~bioact_df.units.str.contains('M')])
print(bioact_df.units.unique())
bioact_df.shape
|
['uM' 'nM' 'M' "10'1 ug/ml" 'ug ml-1' "10'-1microM" "10'1 uM"
"10'-1 ug/ml" "10'-2 ug/ml" "10'2 uM" '/uM' "10'-6g/ml" 'mM' 'umol/L'
'nmol/L']
['uM' 'nM' 'M' "10'-1microM" "10'1 uM" "10'2 uM" '/uM' 'mM']
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Since we deleted some rows, but we want to iterate over the index later, we reset index to be continuous.
|
bioact_df = bioact_df.reset_index(drop=True)
bioact_df.head()
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
To allow further comparison of the IC50 values, we convert all units to nM. First, we write a helper function, which can be applied to the whole dataframe in the next step.
|
def convert_to_NM(unit, bioactivity):
# c=0
# for i, unit in enumerate(bioact_df.units):
if unit != "nM":
if unit == "pM":
value = float(bioactivity)/1000
elif unit == "10'-11M":
value = float(bioactivity)/100
elif unit == "10'-10M":
value = float(bioactivity)/10
elif unit == "10'-8M":
value = float(bioactivity)*10
elif unit == "10'-1microM" or unit == "10'-7M":
value = float(bioactivity)*100
elif unit == "uM" or unit == "/uM" or unit == "10'-6M":
value = float(bioactivity)*1000
elif unit == "10'1 uM":
value = float(bioactivity)*10000
elif unit == "10'2 uM":
value = float(bioactivity)*100000
elif unit == "mM":
value = float(bioactivity)*1000000
elif unit == "M":
value = float(bioactivity)*1000000000
else:
print ('unit not recognized...', unit)
return value
else: return bioactivity
bioactivity_nM = []
for i, row in bioact_df.iterrows():
bioact_nM = convert_to_NM(row['units'], row['value'])
bioactivity_nM.append(bioact_nM)
bioact_df['value'] = bioactivity_nM
bioact_df['units'] = 'nM'
bioact_df.head()
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Compound dataWe have a data frame containing all molecules tested (with the respective measure) against EGFR. Now, we want to get the molecules that are stored behind the respective ChEMBL IDs. Get list of compoundsLet's have a look at the compounds from ChEMBL we have defined bioactivity data for. First, we retrieve ChEMBL ID and structures for the compounds with desired bioactivity data.
|
cmpd_id_list = list(bioact_df['molecule_chembl_id'])
compound_list = compounds.filter(molecule_chembl_id__in = cmpd_id_list) \
.only('molecule_chembl_id','molecule_structures')
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Then, we convert the list to a pandas dataframe and delete duplicates (again, the pandas from_records function might take some time).
|
compound_df = pd.DataFrame.from_records(compound_list)
compound_df = compound_df.drop_duplicates('molecule_chembl_id', keep = 'first')
print(compound_df.shape)
print(bioact_df.shape)
compound_df.head()
|
(4780, 2)
(4780, 11)
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
So far, we have multiple different molecular structure representations. We only want to keep the canonical SMILES.
|
for i, cmpd in compound_df.iterrows():
if compound_df.loc[i]['molecule_structures'] != None:
compound_df.loc[i]['molecule_structures'] = cmpd['molecule_structures']['canonical_smiles']
print (compound_df.shape)
|
(4780, 2)
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Prepare output data Merge values of interest in one dataframe on ChEMBL-IDs:* ChEMBL-IDs* SMILES* units* IC50
|
output_df = pd.merge(bioact_df[['molecule_chembl_id','units','value']], compound_df, on='molecule_chembl_id')
print(output_df.shape)
output_df.head()
|
(4780, 4)
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
For distinct column names, we rename IC50 and SMILES columns.
|
output_df = output_df.rename(columns= {'molecule_structures':'smiles', 'value':'IC50'})
output_df.shape
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
If we do not have a SMILES representation of a compound, we can not further use it in the following talktorials. Therefore, we delete compounds without SMILES column.
|
output_df = output_df[~output_df['smiles'].isnull()]
print(output_df.shape)
output_df.head()
|
(4771, 4)
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
In the next cell, you see that the low IC50 values are difficult to read. Therefore, we prefer to convert the IC50 values to pIC50.
|
output_df = output_df.reset_index(drop=True)
ic50 = output_df.IC50.astype(float)
print(len(ic50))
print(ic50.head(10))
# Convert IC50 to pIC50 and add pIC50 column:
pIC50 = pd.Series()
i = 0
while i < len(output_df.IC50):
value = 9 - math.log10(ic50[i]) # pIC50=-log10(IC50 mol/l) --> for nM: -log10(IC50*10**-9)= 9-log10(IC50)
if value < 0:
print("Negative pIC50 value at index"+str(i))
pIC50.at[i] = value
i += 1
output_df['pIC50'] = pIC50
output_df.head()
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Collected bioactivity data for EGFR Let's have a look at our collected data set. Draw moleculesIn the next steps, we add a molecule column to our datafame and look at the structures of the molecules with the highest pIC50 values.
|
PandasTools.AddMoleculeColumnToFrame(output_df, smilesCol='smiles')
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Sort molecules by pIC50.
|
output_df.sort_values(by="pIC50", ascending=False, inplace=True)
output_df.reset_index(drop=True, inplace=True)
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Show the most active molecules = molecules with the highest pIC50 values.
|
output_df.drop("smiles", axis=1).head()
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Write output fileTo use the data for the following talktorials, we save the data as csv file. Note that it is advisable to drop the molecule column (only contains an image of the molecules) when saving the data.
|
output_df.drop("ROMol", axis=1).to_csv("../data/T1/EGFR_compounds.csv")
|
_____no_output_____
|
CC-BY-4.0
|
talktorials/1_ChEMBL/T1_ChEMBL.ipynb
|
speleo3/TeachOpenCADD
|
Making ML Applications with Gradio[Gradio](https://www.gradio.app/) is a python library that provides web interfaces for your models. This library is very high-level with it being the easiest to learn for beginners. Here we use a dataset called [EMNIST](https://pytorch.org/vision/stable/datasets.htmlemnist) which is an addition to the MNIST(dataset of images with numbers) datasest, by including images of capital and lowercase letters with a total of 62 classes.Using Gradio, an interface is created at the bottom using the model trained in this notebook to accept our drawings of images or numbers to then predict. Importing libraries and Installing Gradio using PIPGoogle does not have Gradio automatically installed on their Google Colab machines, so it is necessary to install it to the specific machine you are using right now. If you choose another runtime machine, it is necessary to repeat this step.**Also, please run this code with a GPU**
|
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Importing PyTorch
import torch
import torch.nn as nn
# Importing torchvision for dataset
import torchvision
import torchvision.transforms as transforms
# Installing gradio using PIP
!pip install gradio
|
Collecting gradio
[?25l Downloading https://files.pythonhosted.org/packages/e4/c6/19d6941437fb56db775b00c0181af81e539c42369bc79c664001d2272ccb/gradio-2.0.5-py3-none-any.whl (1.6MB)
[K |████████████████████████████████| 1.6MB 5.2MB/s
[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from gradio) (3.2.2)
Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from gradio) (1.1.5)
Collecting analytics-python
Downloading https://files.pythonhosted.org/packages/30/81/2f447982f8d5dec5b56c10ca9ac53e5de2b2e9e2bdf7e091a05731f21379/analytics_python-1.3.1-py2.py3-none-any.whl
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from gradio) (1.4.1)
Collecting paramiko
[?25l Downloading https://files.pythonhosted.org/packages/95/19/124e9287b43e6ff3ebb9cdea3e5e8e88475a873c05ccdf8b7e20d2c4201e/paramiko-2.7.2-py2.py3-none-any.whl (206kB)
[K |████████████████████████████████| 215kB 22.1MB/s
[?25hCollecting pycryptodome
[?25l Downloading https://files.pythonhosted.org/packages/ad/16/9627ab0493894a11c68e46000dbcc82f578c8ff06bc2980dcd016aea9bd3/pycryptodome-3.10.1-cp35-abi3-manylinux2010_x86_64.whl (1.9MB)
[K |████████████████████████████████| 1.9MB 22.7MB/s
[?25hCollecting Flask-Cors>=3.0.8
Downloading https://files.pythonhosted.org/packages/db/84/901e700de86604b1c4ef4b57110d4e947c218b9997adf5d38fa7da493bce/Flask_Cors-3.0.10-py2.py3-none-any.whl
Collecting flask-cachebuster
Downloading https://files.pythonhosted.org/packages/74/47/f3e1fedfaad965c81c2f17234636d72f71450f1b4522ca26d2b7eb4a0a74/Flask-CacheBuster-1.0.0.tar.gz
Requirement already satisfied: Flask>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from gradio) (1.1.4)
Collecting markdown2
Downloading https://files.pythonhosted.org/packages/5d/be/3924cc1c0e12030b5225de2b4521f1dc729730773861475de26be64a0d2b/markdown2-2.4.0-py2.py3-none-any.whl
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from gradio) (1.19.5)
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from gradio) (2.23.0)
Collecting ffmpy
Downloading https://files.pythonhosted.org/packages/bf/e2/947df4b3d666bfdd2b0c6355d215c45d2d40f929451cb29a8a2995b29788/ffmpy-0.3.0.tar.gz
Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from gradio) (7.1.2)
Collecting Flask-Login
Downloading https://files.pythonhosted.org/packages/2b/83/ac5bf3279f969704fc1e63f050c50e10985e50fd340e6069ec7e09df5442/Flask_Login-0.5.0-py2.py3-none-any.whl
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->gradio) (1.3.1)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->gradio) (2.4.7)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->gradio) (0.10.0)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->gradio) (2.8.1)
Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->gradio) (2018.9)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from analytics-python->gradio) (1.15.0)
Collecting backoff==1.10.0
Downloading https://files.pythonhosted.org/packages/f0/32/c5dd4f4b0746e9ec05ace2a5045c1fc375ae67ee94355344ad6c7005fd87/backoff-1.10.0-py2.py3-none-any.whl
Collecting monotonic>=1.5
Downloading https://files.pythonhosted.org/packages/9a/67/7e8406a29b6c45be7af7740456f7f37025f0506ae2e05fb9009a53946860/monotonic-1.6-py2.py3-none-any.whl
Collecting pynacl>=1.0.1
[?25l Downloading https://files.pythonhosted.org/packages/9d/57/2f5e6226a674b2bcb6db531e8b383079b678df5b10cdaa610d6cf20d77ba/PyNaCl-1.4.0-cp35-abi3-manylinux1_x86_64.whl (961kB)
[K |████████████████████████████████| 962kB 28.0MB/s
[?25hCollecting cryptography>=2.5
[?25l Downloading https://files.pythonhosted.org/packages/b2/26/7af637e6a7e87258b963f1731c5982fb31cd507f0d90d91836e446955d02/cryptography-3.4.7-cp36-abi3-manylinux2014_x86_64.whl (3.2MB)
[K |████████████████████████████████| 3.2MB 37.5MB/s
[?25hCollecting bcrypt>=3.1.3
[?25l Downloading https://files.pythonhosted.org/packages/26/70/6d218afbe4c73538053c1016dd631e8f25fffc10cd01f5c272d7acf3c03d/bcrypt-3.2.0-cp36-abi3-manylinux2010_x86_64.whl (63kB)
[K |████████████████████████████████| 71kB 9.2MB/s
[?25hRequirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask>=1.1.1->gradio) (1.0.1)
Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask>=1.1.1->gradio) (1.1.0)
Requirement already satisfied: click<8.0,>=5.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=1.1.1->gradio) (7.1.2)
Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=1.1.1->gradio) (2.11.3)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->gradio) (1.24.3)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->gradio) (2.10)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->gradio) (2021.5.30)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->gradio) (3.0.4)
Requirement already satisfied: cffi>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from pynacl>=1.0.1->paramiko->gradio) (1.14.5)
Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask>=1.1.1->gradio) (2.0.1)
Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.4.1->pynacl>=1.0.1->paramiko->gradio) (2.20)
Building wheels for collected packages: flask-cachebuster, ffmpy
Building wheel for flask-cachebuster (setup.py) ... [?25l[?25hdone
Created wheel for flask-cachebuster: filename=Flask_CacheBuster-1.0.0-cp37-none-any.whl size=3372 sha256=2e68e88b4d90e766446a679a8b3c199673be350c949933099f14b55957e7b658
Stored in directory: /root/.cache/pip/wheels/9f/fc/a7/ab5712c3ace9a8f97276465cc2937316ab8063c1fea488ea77
Building wheel for ffmpy (setup.py) ... [?25l[?25hdone
Created wheel for ffmpy: filename=ffmpy-0.3.0-cp37-none-any.whl size=4710 sha256=9da4ad5c3f5cf80dbda1d5ddde11d3b0b7388a9fc462dc27b8e4d3eba882ae2c
Stored in directory: /root/.cache/pip/wheels/cc/ac/c4/bef572cb7e52bfca170046f567e64858632daf77e0f34e5a74
Successfully built flask-cachebuster ffmpy
Installing collected packages: backoff, monotonic, analytics-python, pynacl, cryptography, bcrypt, paramiko, pycryptodome, Flask-Cors, flask-cachebuster, markdown2, ffmpy, Flask-Login, gradio
Successfully installed Flask-Cors-3.0.10 Flask-Login-0.5.0 analytics-python-1.3.1 backoff-1.10.0 bcrypt-3.2.0 cryptography-3.4.7 ffmpy-0.3.0 flask-cachebuster-1.0.0 gradio-2.0.5 markdown2-2.4.0 monotonic-1.6 paramiko-2.7.2 pycryptodome-3.10.1 pynacl-1.4.0
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
Downloading and Preparing EMNIST Dataset**Note:** Even though the images in the EMNIST dataset are 28x28 images just like the regular MNIST dataset, there are necessary transforms needed for EMNIST dataset. If not transformed, the images are rotated 90° counter-clockwise and are flipped vertically. To undo these two issues, we first rotate it 90° counter-clockwise and then flip it horizontallyHere is the image before processing:Here is the image after processing:
|
# Getting Dataset
!mkdir EMNIST
root = '/content/EMNIST'
# Creating Transforms
transforms = transforms.Compose([
# Rotating image 90 degrees counter-clockwise
transforms.RandomRotation((-90,-90)),
# Flipping images horizontally
transforms.RandomHorizontalFlip(p=1),
# Converting images to tensor
transforms.ToTensor()
])
# Getting dataset
training_dataset = torchvision.datasets.EMNIST(root,
split='byclass',
train=True,
download=True,
transform=transforms)
test_dataset = torchvision.datasets.EMNIST(root,
split='byclass',
train=False,
download=True,
transform=transforms)
# Loading Dataset into dataloaders
batch_size = 2048
training_dataloader = torch.utils.data.DataLoader(training_dataset, batch_size=batch_size, shuffle=True)
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
# Getting shapes of dataset
print('Shape of the training dataset:', training_dataset.data.shape)
print('Shape of the test dataset:', test_dataset.data.shape)
# Getting reverted class_to_idx dictionary to get classes by idx
idx_to_class = {val:key for key, val in training_dataset.class_to_idx.items()}
# Plotting 5 images with classes
plt.figure(figsize=(10,2))
for i in range(5):
plt.subplot(1,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(training_dataset[i][0].squeeze().numpy(), cmap=plt.cm.binary)
plt.xlabel(idx_to_class[training_dataset[i][1]])
|
Downloading and extracting zip archive
Downloading https://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip to /content/EMNIST/EMNIST/raw/emnist.zip
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
Building the Model
|
class Neural_Network(nn.Module):
# Constructor
def __init__(self, num_classes):
super(Neural_Network, self).__init__()
# Defining Fully-Connected Layers
self.fc1 = nn.Linear(28*28, 392) # 28*28 since each image is 28*28
self.fc2 = nn.Linear(392, 196)
self.fc3 = nn.Linear(196, 98)
self.fc4 = nn.Linear(98, num_classes)
# Activation function
self.relu = nn.ReLU()
def forward(self, x):
# Need to flatten each image in the batch
x = x.flatten(start_dim=1)
# Input it into the Fully connected layers
x = self.relu(self.fc1(x))
x = self.relu(self.fc2(x))
x = self.relu(self.fc3(x))
x = self.fc4(x)
return x
# Getting number of classes
num_classes = len(idx_to_class)
model = Neural_Network(num_classes)
print(model)
|
Neural_Network(
(fc1): Linear(in_features=784, out_features=392, bias=True)
(fc2): Linear(in_features=392, out_features=196, bias=True)
(fc3): Linear(in_features=196, out_features=98, bias=True)
(fc4): Linear(in_features=98, out_features=62, bias=True)
(relu): ReLU()
)
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
Defining Loss Function and Optimizer
|
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
|
_____no_output_____
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
Moving model to GPUIf you have not changed the runtime type to a GPU, please do so now. This helps with the speed of training.
|
# Use GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
# Moving model to use GPU
model.to(device)
|
_____no_output_____
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
Training the Model
|
# Function that returns a torch tensor with predictions to compare with labels
def get_preds_from_logits(logits):
# Using softmax to get an array that sums to 1, and then getting the index with the highest value
return torch.nn.functional.softmax(logits, dim=1).argmax(dim=1)
epochs = 10
train_losses = []
train_accuracies = []
for epoch in range(1, epochs+1):
train_loss = 0.0
train_counts = 0
###################
# train the model #
###################
# Setting model to train mode
model.train()
for images, labels in training_dataloader:
# Moving data to GPU if available
images, labels = images.to(device), labels.to(device)
# Setting all gradients to zero
optimizer.zero_grad()
# Calculate Output
output = model(images)
# Calculate Loss
loss = criterion(output, labels)
# Calculate Gradients
loss.backward()
# Perform Gradient Descent Step
optimizer.step()
# Saving loss
train_loss += loss.item()
# Get Predictions
train_preds = get_preds_from_logits(output)
# Saving number of right predictions for accuracy
train_counts += train_preds.eq(labels).sum().item()
# Averaging and Saving Losses
train_loss/=len(training_dataset)
train_losses.append(train_loss)
# Getting accuracies and saving them
train_acc = train_counts/len(training_dataset)
train_accuracies.append(train_acc)
print('Epoch: {} \tTraining Loss: {:.6f} \tTraining Accuracy: {:.2f}%'.format(epoch, train_loss, train_acc*100))
plt.plot(train_losses)
plt.xlabel('epoch')
plt.ylabel('Mean Squared Error')
plt.title('Training Loss')
plt.show()
plt.plot(train_accuracies)
plt.xlabel('epoch')
plt.ylabel('Accuracy')
plt.title('Training Accuracy')
plt.show()
|
_____no_output_____
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
Evaluating the modelHere we will display the test loss and accuracy and examples of images that were misclassified.
|
test_loss = 0.0
test_counts = 0
# Setting model to evaluation mode, no parameters will change
model.eval()
for images, labels in test_dataloader:
# Moving to GPU if available
images, labels = images.to(device), labels.to(device)
# Calculate Output
output = model(images)
# Calculate Loss
loss = criterion(output, labels)
# Saving loss
test_loss += loss.item()
# Get Predictions
test_preds = get_preds_from_logits(output)
# Saving number of right predictions for accuracy
test_counts += test_preds.eq(labels).sum().item()
# Calculating test accuracy
test_acc = test_counts/len(test_dataset)
print('Test Loss: {:.6f} \tTest Accuracy: {:.2f}%'.format(test_loss, test_acc*100))
import torchvision.transforms as transforms
# Have to another set of transforms to rotate and flip testing data
test_transforms = transforms.Compose([
# Rotating image 90 degrees counter-clockwise
transforms.RandomRotation((-90,-90)),
# Flipping images horizontally
transforms.RandomHorizontalFlip(p=1)
])
# Transforming the data and normalizing them
test_images = test_transforms(test_dataset.data).to(device)/255
# Getting Predictions
predictions = get_preds_from_logits(model(test_images))
# Getting Labels
test_labels = test_dataset.targets.to(device)
# Getting misclassified booleans
correct_bools = test_labels.eq(predictions)
misclassified_indices = []
for i in range(len(correct_bools)):
if correct_bools[i] == False:
misclassified_indices.append(i)
# Plotting 5 misclassified images
plt.figure(figsize=(10,2))
for i in range(5):
idx = misclassified_indices[i]
plt.subplot(1,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(test_images[idx].squeeze().cpu().numpy(), cmap=plt.cm.binary)
true_label = idx_to_class[test_labels[idx].item()]
pred_label = idx_to_class[predictions[idx].item()]
plt.xlabel(f'True: {true_label}, Pred: {pred_label}')
|
_____no_output_____
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
How to use GradioThere are three parts of using Gradio1. Define a function that takes input and returns your model's output2. Define what type of input the interface will use3. Define what type of output the interface will giveThe function `recognize_image` takes a 28x28 image that is not yet normalized and returns a dictionary with the keys being the classes and the values being the probabilities for that class.The class [`gradio.inputs.Image`](https://www.gradio.app/docsi_image) is used as the input that provides a window in the Gradio interface, but there are many customizations you can provide.These are some the parameters:1. shape - (width, height) shape to crop and resize image to; if None, matches input image size.2. image_mode - "RGB" if color, or "L" if black and white.3. invert_colors - whether to invert the image as a preprocessing step.4. source - Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools.The class [gradio.outputs.Label](https://www.gradio.app/docso_label) is used as the output, which provides probabilities to the interface for the purpose of displaying them.These are the parameters:1. num_top_classes - number of most confident classes to show.2. type - Type of value to be passed to component. "value" expects a single out label, "confidences" expects a dictionary mapping labels to confidence scores, "auto" detects return type.3. label - component name in interface.The interface class [gradio.Interface](https://www.gradio.app/docsinterface) is responsible of creating the interface that compiles the type of inputs and outputs. There is a `.launch()` method that launches the interface in this notebook after compiling.These are the parameters used in this interface:1. fn - the function to wrap an interface around.2. inputs - a single Gradio input component, or list of Gradio input components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of input components should match the number of parameters in fn.3. outputs - a single Gradio output component, or list of Gradio output components. Components can either be passed as instantiated objects, or referred to by their string shortcuts. The number of output components should match the number of values returned by fn.4. title - a title for the interface; if provided, appears above the input and output components.5. description - a description for the interface; if provided, appears above the input and output components.6. live - whether the interface should automatically reload on change.7. interpretation - function that provides interpretation explaining prediction output. Pass "default" to use built-in interpreter.I will enourage you to view the [documentation](https://www.gradio.app/docs) for the interface, inputs and outputs, you can find all the information you need there. It is helpful to refer to the documentation to understand other parameters that are not used in this lesson.
|
import gradio
import gradio as gr
# Function that returns a torch tensor with predictions to compare with labels
def get_probs_from_logits(logits):
# Using softmax to get probabilities from the logits
return torch.nn.functional.softmax(logits, dim=1)
# Function that takes the img drawn in the Gradio interface, then gives probabilities
def recognize_image(img):
# Normalizes inputted image and converts it to a tensor for the model
img = torch.tensor(img/255, dtype=torch.float).unsqueeze(dim=0).to(device)
# Getting output
output = model(img)
# Getting probabilites of the image
probabilities = get_probs_from_logits(output).flatten()
# Returns a dictionary with the key being the class and val being the probability
probabilities_dict = {idx_to_class[i]:probabilities[i].item() for i in range(num_classes)}
return probabilities_dict
im = gradio.inputs.Image(shape=(28, 28),
image_mode='L',
invert_colors=True,
source="canvas")
title = "Number and Letter Classifier App"
description = """This app is able to guess the number or letter you draw below.
The ML model was trained on the EMNIST dataset, please use below!"""
iface = gr.Interface(fn=recognize_image,
inputs=im,
outputs=gradio.outputs.Label(num_top_classes=5),
title=title,
description=description,
live=True,
interpretation="default")
iface.launch()
|
Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`
This share link will expire in 24 hours. If you need a permanent link, visit: https://gradio.app/introducing-hosted (NEW!)
Running on External URL: https://27407.gradio.app
Interface loading below...
|
MIT
|
machine_learning/lesson 4 - ML Apps/Gradio/EMNIST_Gradio_Tutorial.ipynb
|
BreakoutMentors/Data-Science-and-Machine-Learning
|
Baixando a base de dados do Kaggle
|
# baixando a lib do kaggle
!pip install --upgrade kaggle
!pip install plotly
# para visualizar dados faltantes
!pip install missingno
# requisitando upload do token de autentificação do Kaggle
# OBS: o arquivo kaggle.json precisa ser baixado da sua conta pessoal do Kaggle.
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(name=fn, length=len(uploaded[fn]) ))
# alocando o arquivo kaggle.json em seu devido local e permitindo escrita e leitura no mesmo
!mkdir -p ~/.kaggle
!mv kaggle.json ~/.kaggle
!chmod 600 ~/.kaggle/kaggle.json
!kaggle datasets download -d gpreda/covid-world-vaccination-progress
!!unzip covid-world-vaccination-progress.zip -d data_folder
|
Downloading covid-world-vaccination-progress.zip to /content
0% 0.00/172k [00:00<?, ?B/s]
100% 172k/172k [00:00<00:00, 20.4MB/s]
|
MIT
|
src/projeto_1_ciencia_de_dados.ipynb
|
Cogitus/covid-vacine-progression-analysis
|
Código da análise exploratória em si
|
import pandas as pd
import matplotlib.pyplot as plt
import missingno as msno
import plotly.graph_objects as go
import matplotlib.ticker as ticker
def gera_lista_vacinas(dataset):
'''
Gera uma lista com todas as vacinas do dataset
input: DataFrame dos dados
output: lista de todas as vacinas
'''
todas_vacinas = list(dataset.groupby(['vacinas']).count().index)
conjunto_vacinas = set()
for lista_vacinas in todas_vacinas:
lista_vacinas = lista_vacinas.split(', ')
for vacina in lista_vacinas:
conjunto_vacinas.add(vacina)
lista_vacinas = list(conjunto_vacinas)
lista_vacinas.sort()
return lista_vacinas
def gera_lista_paises(dataset):
'''
Gera a lista de países que estão em vacinação
input: DataFrame dos dados
output: lista de todos os países
'''
return list(dataset.groupby(['pais']).count().index)
def gera_dataframe_vacinas(dataset):
'''
Gera um novo DataFrame em que as vacinas antes eram listadas na coluna 'vacinas'
agora são listadas entre 10 colunas correspondentes à cada vacina, com 0's e 1's.
Os 1's representam que vacina está sendo aplicada naquele país, os 0's que não!
input: DataFrame dos dados
output: DataFrame dos dados das vacinas categorizados
'''
labels_vacinas = gera_lista_vacinas(dataset) # lista das vacinas entendidas como labels
dataset_vacinas = dataset['vacinas']
array_temporario_vacinas = [] # inicia como uma lista vazia
for linha_vacina in dataset_vacinas:
sublista_vacinas = linha_vacina.split(', ')
#lista de tamanho len(labels_vacinas) com 0's para elementos em sublista
nova_linha = [int(vacina in sublista_vacinas) for vacina in labels_vacinas]
array_temporario_vacinas.append(nova_linha)
dataset_temporario_vacinas = pd.DataFrame(array_temporario_vacinas, columns=labels_vacinas)
dataset.drop(columns=['vacinas'], axis=1, inplace=True)
dataset = pd.concat([dataset, dataset_temporario_vacinas], axis=1)
return dataset
dataset = pd.read_csv(r'data_folder/country_vaccinations.csv')
nome_colunas = ['pais', 'codigo_iso', 'data', 'total_vacinacoes', 'pessoas_vacinadas',
'pessoas_tot_vacinadas', 'vacinacoes_diarias_raw', 'vacinacoes_diarias',
'tot_vacinacoes_por_cent', 'pessoas_vacinadas_por_cent', 'pessoas_tot_vacinadas_por_cent',
'vacinacoes_diarias_por_milhao', 'vacinas', 'fonte_dados', 'website_fonte']
nome_colunas_antigo = list(dataset.columns)
dataset.rename(columns=dict(zip(nome_colunas_antigo, nome_colunas)), inplace=True)
dataset.head()
# DATAFRAME COM AS INFOS DAS VACINAS
freq_vacinas = dataset.groupby('pais').max()
demais_colunas = [coluna for coluna in nome_colunas if coluna not in lista_vacinas and coluna not in ['pais', 'vacinas']]
freq_vacinas.drop(columns=demais_colunas, axis=1, inplace=True)
# para o bar plot vacinas x num_paises
densidade_vacinas = pd.DataFrame(freq_vacinas.sum(), columns=['num_paises'])
# BARPLOT DAS VACINAS
fig_disposicao_vacinas = plt.figure(figsize = (20, 10))
plt.title('Número de países que utilizam as vacinas', fontsize=18)
y_label = densidade_vacinas.index
x_label = densidade_vacinas['num_paises'].values
plt.bar(y_label, x_label)
plt.grid()
for i in range(len(x_label)):
plt.annotate(str(x_label[i]), xy=(y_label[i], x_label[i]), ha='center', va='bottom', fontsize=14)
plt.show()
# dados faltantes de todo o banco de dados
msno.matrix(dataset)
# Vamos visualizar a distribuição de dados faltantes POR PAÍS
from math import floor
# caso dê problema, é possível que um novo país tenha sido adicionado!
num_rows = 25
num_columns = 6
fig, axarr = plt.subplots(num_rows, num_columns, figsize=(24, 90))
lista_paises = gera_lista_paises(dataset)
for pais in enumerate(lista_paises):
# extraindo nome e numero do pais
num_pais = pais[0]
nome_pais = pais[1]
# definindo coordenadas de onde no subplot será plotado
x_plot = floor(num_pais/num_columns)
y_plot = num_pais % num_columns
axarr[x_plot][y_plot].set_title(nome_pais)
msno.matrix(dataset[dataset['pais'] == nome_pais], ax=axarr[x_plot][y_plot], labels=False)
dataset.describe()
|
_____no_output_____
|
MIT
|
src/projeto_1_ciencia_de_dados.ipynb
|
Cogitus/covid-vacine-progression-analysis
|
Código da criação dos gráficos e mapas
|
groupby_country = dataset.groupby(['pais'])
listof_dataframe_countries = []
for idx, group in enumerate(groupby_country):
listof_dataframe_countries.append(group)
total_vac_top_countries = pd.DataFrame()
# total_vacinacoes pessoas_vacinadas pessoas_tot_vacinadas
for i in range(len(listof_dataframe_countries)):
country_df = listof_dataframe_countries[i][1]
filtered_df = country_df[country_df['total_vacinacoes'].notna()]
latest_day_data = filtered_df.iloc[-1:]
total_vac_top_countries = total_vac_top_countries.append(latest_day_data, ignore_index=True)
total_vac_top_countries = total_vac_top_countries.sort_values(by=['total_vacinacoes'], ascending=False)
fig, axes = plt.subplots(nrows=2, ncols=5)
i = 0
j = 0
for pais in total_vac_top_countries.head(10).iterrows():
country = dataset[dataset['pais'] == pais[1]['pais']]
filtered = country[country['total_vacinacoes'].notna()].reset_index()
fig2 = filtered[['total_vacinacoes','pessoas_vacinadas','pessoas_tot_vacinadas']].plot(title=pais[1]['pais'], ax=axes[j][i], grid=True)
fig2.yaxis.set_major_formatter(ticker.EngFormatter())
i+=1
if(i%5 == 0):
j+=1
i=0
plt.show()
fig, axes = plt.subplots(nrows=2, ncols=5)
i = 0
j = 0
for pais in total_vac_top_countries.head(10).iterrows():
country = dataset[dataset['pais'] == pais[1]['pais']]
filtered = country[country['tot_vacinacoes_por_cent'].notna()].reset_index()
fig2 = filtered[['tot_vacinacoes_por_cent','pessoas_vacinadas_por_cent','pessoas_tot_vacinadas_por_cent']].plot(title=pais[1]['pais'], ax=axes[j][i], grid=True)
fig2.yaxis.set_major_formatter(ticker.PercentFormatter())
fig2.set_ylim(0, 100)
fig2.legend(('Total doses', 'Pessoas vacinadas', 'Pessoas imunizadas'))
i+=1
if(i%5 == 0):
j+=1
i=0
plt.show()
for i in range(len(listof_dataframe_countries)):
country_name = listof_dataframe_countries[i][0]
if(country_name in ["United States", "Austria", "Brazil", "United Kingdom"]):
country_df = listof_dataframe_countries[i][1]
filtered_df = country_df[country_df['total_vacinacoes'].notna()]
filtered_df[['total_vacinacoes','pessoas_vacinadas','pessoas_tot_vacinadas']].plot(title=country_name)
plt.show()
df = pd.DataFrame()
for i in range(len(listof_dataframe_countries)):
country_name = listof_dataframe_countries[i][0]
country_df = listof_dataframe_countries[i][1]
filtered_df = country_df[country_df['pessoas_vacinadas_por_cent'].notna()]
latest_day_data = filtered_df.iloc[-1:]
df = df.append(latest_day_data, ignore_index=True)
df.to_csv('./pessoas_vacinadas_por_cent.csv')
fig_pessoas_vacinadas = go.Figure(data=go.Choropleth(
locations = df['codigo_iso'],
z = df['pessoas_vacinadas_por_cent'],
text = df['pais'],
colorscale = 'YlGnBu',
autocolorscale=False,
marker_line_width=0.5,
colorbar_title = '% pessoas<br>vacinadas',
))
config = {
'modeBarButtonsToRemove': ['lasso2d','zoomInGeo','zoomOutGeo']
}
fig_pessoas_vacinadas.update_layout(
title_text='Covid-19 World Vaccination - Porcentagem de pessoas que tomaram pelo menos uma dose da vacina',
geo=dict(
showframe=False,
showcoastlines=False,
projection_type='equirectangular'
)
)
fig_pessoas_vacinadas.data[0].update(zmin=0, zmax=60)
fig_pessoas_vacinadas.show(config=config)
df2 = pd.DataFrame()
for i in range(len(listof_dataframe_countries)):
country_name = listof_dataframe_countries[i][0]
country_df = listof_dataframe_countries[i][1]
filtered_df = country_df[country_df['total_vacinacoes'].notna()]
latest_day_data = filtered_df.iloc[-1:]
df2 = df2.append(latest_day_data, ignore_index=True)
df2.to_csv('./total_vacinacoes.csv')
fig_total_doses = go.Figure(data=go.Choropleth(
locations = df2['codigo_iso'],
z = df2['total_vacinacoes'],
text = df2['pais'],
colorscale = 'Blues',
autocolorscale=False,
marker_line_width=0.5,
colorbar_title = 'Total<br>vacinas<br>(milhões)',
))
config = {
'modeBarButtonsToRemove': ['lasso2d','zoomInGeo','zoomOutGeo']
}
fig_total_doses.update_layout(
title_text='Covid-19 World Vaccination - Total de doses aplicadas',
geo=dict(
showframe=False,
showcoastlines=False,
projection_type='equirectangular'
)
)
fig_total_doses.show(config=config)
df3 = pd.DataFrame()
for i in range(len(listof_dataframe_countries)):
country_name = listof_dataframe_countries[i][0]
country_df = listof_dataframe_countries[i][1]
filtered_df = country_df[country_df['vacinacoes_diarias_por_milhao'].notna()]
latest_day_data = filtered_df.iloc[-1:]
df3 = df3.append(latest_day_data, ignore_index=True)
df3.to_csv('./vac_diarias_milhao.csv')
fig_vac_diarias_milhao = go.Figure(data=go.Choropleth(
locations = df3['codigo_iso'],
z = df3['vacinacoes_diarias_por_milhao'],
text = df3['pais'],
colorscale = 'YlGnBu',
autocolorscale=False,
reversescale=False,
marker_line_width=0.5,
colorbar_title = 'vacinações<br>diárias<br>p/ milhão',
))
config = {
'modeBarButtonsToRemove': ['lasso2d','zoomInGeo','zoomOutGeo']
}
fig_vac_diarias_milhao.update_layout(
title_text='Covid-19 World Vaccination - Vacinações diárias por milhão',
geo=dict(
showframe=False,
showcoastlines=False,
projection_type='equirectangular'
)
)
fig_vac_diarias_milhao.data[0].update(zmin=500, zmax=15000)
fig_vac_diarias_milhao.show(config=config)
|
_____no_output_____
|
MIT
|
src/projeto_1_ciencia_de_dados.ipynb
|
Cogitus/covid-vacine-progression-analysis
|
Backprop Core Example: Text SummarisationText summarisation takes a chunk of text, and extracts the key information.
|
# Set your API key to do inference on Backprop's platform
# Leave as None to run locally
api_key = None
import backprop
summarisation = backprop.Summarisation(api_key=api_key)
# Change this up.
input_text = """
Britain began its third COVID-19 lockdown on Tuesday with the government calling for one last major national effort to defeat the spread of a virus that has infected an estimated one in 50 citizens before mass vaccinations turn the tide.
Finance minister Rishi Sunak announced a new package of business grants worth 4.6 billion pounds ($6.2 billion) to help keep people in jobs and firms afloat until measures are relaxed gradually, at the earliest from mid-February but likely later.
Britain has been among the countries worst-hit by COVID-19, with the second highest death toll in Europe and an economy that suffered the sharpest contraction of any in the Group of Seven during the first wave of infections last spring.
Prime Minister Boris Johnson said the latest data showed 2% of the population were currently infected - more than a million people in England.
“When everybody looks at the position, people understand overwhelmingly that we have no choice,” he told a news conference.
More than 1.3 million people in Britain have already received their first dose of a COVID-19 vaccination, but this is not enough to have an impact on transmission yet.
Johnson announced the new lockdown late on Monday, saying the highly contagious new coronavirus variant first identified in Britain was spreading so fast the National Health Service risked being overwhelmed within 21 days.
In England alone, some 27,000 people are in hospital with COVID, 40% more than during the first peak in April, with infection numbers expected to rise further after increased socialising during the Christmas period.
Since the start of the pandemic, more than 75,000 people have died in the United Kingdom within 28 days of testing positive for coronavirus, according to official figures. The number of daily new infections passed 60,000 for the first time on Tuesday.
A Savanta-ComRes poll taken just after Johnson’s address suggested four in five adults in England supported the lockdown.
“I definitely think it was the right decision to make,” said Londoner Kaitlin Colucci, 28. “I just hope that everyone doesn’t struggle too much with having to be indoors again.”
Downing Street said Johnson had cancelled a visit to India later this month to focus on the response to the virus, and Buckingham Palace called off its traditional summer garden parties this year.
nder the new rules in England, schools are closed to most pupils, people should work from home if possible, and all hospitality and non-essential shops are closed. Semi-autonomous executives in Scotland, Wales and Northern Ireland have imposed similar measures.
As infection rates soar across Europe, other countries are also clamping down on public life. Germany is set to extend its strict lockdown until the end of the month, and Italy will keep nationwide restrictions in place this weekend while relaxing curbs on weekdays.
Sunak’s latest package of grants adds to the eye-watering 280 billion pounds in UK government support already announced for this financial year to stave off total economic collapse.
The new lockdown is likely to cause the economy to shrink again, though not as much as during the first lockdown last spring. JP Morgan economist Allan Monks said he expected the economy to shrink by 2.5% in the first quarter of 2021 -- compared with almost 20% in the second quarter of 2020.
To end the cycle of lockdowns, the government is pinning its hopes on vaccines. It aims to vaccinate all elderly care home residents and their carers, everyone over the age of 70, all frontline health and social care workers, and everyone who is clinically extremely vulnerable by mid-February.
"""
summary = summarisation(input_text)
print(summary)
|
Britain begins its third COVID-19 lockdown. Finance minister Rishi Sunak announces a package of business grants. The government is pinning its hopes on vaccines.
|
Apache-2.0
|
examples/Summarisation.ipynb
|
lucky7323/backprop
|
Jacobian calculation
|
x = pdt.Var('x')
y = pdt.Var('y')
gm = pdt.Par('gm')
a = pdt.Par('a')
b = pdt.Par('b')
t = pdt.Var('t')
xdot = pdt.Fun(y, [y], 'xdot')
ydot = pdt.Fun(-a*gm*gm - b*gm*gm*x -gm*gm*x*x*x -gm*x*x*y + gm*gm*x*x - gm*x*y, [x, y], 'ydot')
F = pdt.Fun([xdot(y), ydot(x, y)], [x,y], 'F')
jac = pdt.Fun(pdt.Diff(F, [x, y]), [t, x, y], 'Jacobian')
jac.simplify()
print(jac.eval(t=t, x=x, y=y))
|
[[0,1],[(((-b*gm*gm)-gm*gm*(x*x+x*2*x))-gm*(x*y+x*y)+gm*gm*2*x)-gm*y,(-gm*x*x)-gm*x]]
|
MIT
|
pydstools_implementation.ipynb
|
gnouveau/birdsynth
|
Simple model
|
icdict = {'x': 0, 'y': 0}
pardict = {
'gm': 2 # g is γ in Boari 2015
}
vardict = {
'x': xdot(y),
'y': ydot(x,y),
}
args = pdt.args()
args.name = 'birdsynth'
args.fnspecs = [jac, xdot, ydot]
args.ics = icdict
args.pars = pardict
args.inputs = inputs
args.tdata = [0, 1]
args.varspecs = vardict
ds = pdt.Generator.Vode_ODEsystem(args)
ds.haveJacobian()
traj = ds.compute('demo')
plt.plot(traj.sample(dt=1/(44100*20))['x'])
auxdict = {'Pi':(['t', 'x', 'a_'], 'if(t > 0, a_ * x - r * 1, 0)'),
'Pt':(['t', 'x', 'a_'], '(1 - r) * Pi(t - 0.5 * T, x, a_)')
}
icdict = {'x': 0, 'y': 0, 'o1':0, 'i1':0, 'i3':0}
pardict = {'g': 2400, # g is γ in Boari 2015
'T': 0.2,
'r': 0.1,
'a_p': -540e6,
'b_p': -7800,
'c_p': 1.8e8,
'd_p': 1.2e-2,
'e_p': 7.2e-1,
'f_p': -0.83e-2,
'g_p': -5e2,
'h_p': 1e-4
}
vardict = {'x': 'y',
'y': '-a*Pow(g, 2) - b * Pow(g, 2) * x - Pow(g, 2) * Pow(x, 3) - g * Pow(x, 2) * y + Pow(g, 2) * x * x'
'- g * x * y',
'i1': 'o1',
'o1': 'a_p * i1 + b_p * o1 + c_p * i3 + d_p * Pt(t, x, a) + e_p * Pt(t, x, a)',
'i3': 'f_p * o1 + g_p * i3 + h_p * Pt(t, x, a)'
}
args = pdt.args()
args.name = 'birdsynth'
args.ics = icdict
args.pars = pardict
args.fnspecs = auxdict
args.inputs = inputs
args.tdata = [0, len(ab)/44100]
args.varspecs = vardict
ds = pdt.Generator.Vode_ODEsystem(args)
traj = ds.compute('demo')
pts = traj.sample(dt=1/(44100))
plt.plot(pts['t'], pts['x'])
x = ds.variables['x']
y_0 = pdt.Var('-a*Pow(g, 2) - b * Pow(g, 2) * x - Pow(g, 2) * Pow(x, 3) - g * Pow(x, 2) * y + Pow(g, 2) * x * x'
'- g * x * y', 'y_0')
Pi(2)
|
_____no_output_____
|
MIT
|
pydstools_implementation.ipynb
|
gnouveau/birdsynth
|
05 超参数
|
import numpy as np
from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
y = digits.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)
from sklearn.neighbors import KNeighborsClassifier
knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train, y_train)
knn_clf.score(X_test, y_test)
|
_____no_output_____
|
Apache-2.0
|
04-kNN/05-Hyper-Parameters/05-Hyper-Parameters.ipynb
|
mtianyan/Mtianyan-Play-with-Machine-Learning-Algorithms
|
寻找最好的k
|
best_score = 0.0
best_k = -1
for k in range(1, 11):
knn_clf = KNeighborsClassifier(n_neighbors=k)
knn_clf.fit(X_train, y_train)
score = knn_clf.score(X_test, y_test)
if score > best_score:
best_k = k
best_score = score
print("best_k =", best_k)
print("best_score =", best_score)
|
best_k = 4
best_score = 0.991666666667
|
Apache-2.0
|
04-kNN/05-Hyper-Parameters/05-Hyper-Parameters.ipynb
|
mtianyan/Mtianyan-Play-with-Machine-Learning-Algorithms
|
考虑距离？不考虑距离？
|
best_score = 0.0
best_k = -1
best_method = ""
for method in ["uniform", "distance"]:
for k in range(1, 11):
knn_clf = KNeighborsClassifier(n_neighbors=k, weights=method)
knn_clf.fit(X_train, y_train)
score = knn_clf.score(X_test, y_test)
if score > best_score:
best_k = k
best_score = score
best_method = method
print("best_method =", best_method)
print("best_k =", best_k)
print("best_score =", best_score)
sk_knn_clf = KNeighborsClassifier(n_neighbors=4, weights="distance", p=1)
sk_knn_clf.fit(X_train, y_train)
sk_knn_clf.score(X_test, y_test)
|
_____no_output_____
|
Apache-2.0
|
04-kNN/05-Hyper-Parameters/05-Hyper-Parameters.ipynb
|
mtianyan/Mtianyan-Play-with-Machine-Learning-Algorithms
|
搜索明可夫斯基距离相应的p
|
best_score = 0.0
best_k = -1
best_p = -1
for k in range(1, 11):
for p in range(1, 6):
knn_clf = KNeighborsClassifier(n_neighbors=k, weights="distance", p=p)
knn_clf.fit(X_train, y_train)
score = knn_clf.score(X_test, y_test)
if score > best_score:
best_k = k
best_p = p
best_score = score
print("best_k =", best_k)
print("best_p =", best_p)
print("best_score =", best_score)
|
best_k = 3
best_p = 2
best_score = 0.988888888889
|
Apache-2.0
|
04-kNN/05-Hyper-Parameters/05-Hyper-Parameters.ipynb
|
mtianyan/Mtianyan-Play-with-Machine-Learning-Algorithms
|
Bayesian Hierarchical ModelingThis jupyter notebook accompanies the Bayesian Hierarchical Modeling lecture(s) delivered by Stephen Feeney as part of David Hogg's [Computational Data Analysis class](http://dwh.gg/FlatironCDA). As part of the lecture(s) you will be asked to complete a number of tasks, some of which will involve direct coding into the notebook; these sections are marked by task. This notebook requires numpy, matplotlib, scipy, [corner](https://github.com/sfeeney/bhm_lecture.git), [pystan](https://pystan.readthedocs.io/en/latest/getting_started.html) and pickle to run (the last two are required solely for the final task).The model we're going to be inferring is below.We start with imports...
|
from __future__ import print_function
# make sure everything we need is installed if running on Google Colab
def is_colab():
try:
cfg = get_ipython().config
if cfg['IPKernelApp']['kernel_class'] == 'google.colab._kernel.Kernel':
return True
else:
return False
except NameError:
return False
if is_colab():
!pip install --quiet numpy matplotlib scipy corner pystan
import numpy as np
import numpy.random as npr
import matplotlib.pyplot as mp
%matplotlib inline
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
... and immediately move to... Task 2In which I ask you to write a Python function to generate a simulated Cepheid sample using the period-luminosity relation $m_{ij} = \mu_i + M^* + s\,\log p_{ij} + \epsilon(\sigma_{\rm int})$. For simplicity, assume Gaussian priors on everything, Gaussian intrinsic scatter and Gaussian measurement uncertainties. Assume only the first host has a distance modulus estimate.
|
# setup
n_gal = 2
n_star = 200
n_samples = 50000
# PL relation parameters
abs_bar = -26.0 # mean of standard absolute magnitude prior
abs_sig = 4.0 # std dev of standard absolute magnitude prior
s_bar = -1.0 # mean of slope prior
s_sig = 1.0 # std dev of slope prior
mu_bar = 30.0 # mean of distance modulus prior
mu_sig = 5.0 # std dev of distance modulus prior
m_sig_int = 0.05 # intrinsic scatter, assumed known
# uncertainties
mu_hat_sig = 0.01 # distance modulus measurement uncertainty
m_hat_sig = 0.02 # apparent magnitude measurement uncertainty
def simulate(n_gal, n_star, abs_bar, abs_sig, s_bar, s_sig, mu_bar, mu_sig, mu_hat_sig, m_sig_int, m_hat_sig):
# draw CPL parameters from Gaussian prior with means abs_bar and s_bar and standard deviations
# abs_sig and s_sig
#abs_true = abs_bar
#s_true = s_bar
abs_true = abs_bar + npr.randn() * abs_sig
s_true = s_bar + npr.randn() * s_sig
# draw n_gal distance moduli from Gaussian prior with mean mu_bar and standard deviation mu_sig
# i've chosen to sort here so the closest galaxy is the one with the measured distance modulus
mu_true = np.sort(mu_bar + npr.randn(n_gal) * mu_sig)
# measure ONLY ONE galaxy's distance modulus noisily. the noise here is assumed Gaussian with
# zero mean and standard deviation mu_hat_sig
mu_hat = mu_true[0] + npr.randn() * mu_hat_sig
# draw log periods. these are assumed to be perfectly observed in this model, so they
# are simply a set of pre-specified numbers. i have chosen to generate new values with
# each simulation, drawn such that log-periods are uniformly drawn in the range 1-2 (i.e.,
# 10 to 100 days). you can have these for free!
lp_true = 1.0 + npr.rand(n_gal, n_star)
# draw true apparent magnitudes. these are distributed around the Cepheid period-luminosity
# relation with Gaussian intrinsic scatter (mean 0, standard deviation m_sig_int)
m_true = np.zeros((n_gal, n_star))
for i in range(n_gal):
m_true[i, :] = mu_true[i] + abs_true + s_true * lp_true[i, :] + npr.randn(n_star) * m_sig_int
# measure the apparent magnitudes noisily, all with the same measurement uncertainty m_hat_sig
m_hat = m_true + npr.randn(n_gal, n_star) * m_hat_sig
# return!
return (abs_true, s_true, mu_true, lp_true, m_true, mu_hat, m_hat)
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Let's check that the simulation generates something sane. A simple test that the magnitude measurements errors are correctly generated.
|
# simulate
abs_true, s_true, mu_true, lp_true, m_true, mu_hat, m_hat = \
simulate(n_gal, n_star, abs_bar, abs_sig, s_bar, s_sig, mu_bar, mu_sig, mu_hat_sig, m_sig_int, m_hat_sig)
# plot difference between true and observed apparent magnitudes. this should be the
# noise, which is Gaussian distributed with mean zero and std dev m_hat_sig
outs = mp.hist((m_true - m_hat).flatten())
dm_grid = np.linspace(np.min(outs[1]), np.max(outs[1]))
mp.plot(dm_grid, np.exp(-0.5 * (dm_grid/m_hat_sig) ** 2) * np.max(outs[0]))
mp.xlabel(r'$m_{ij} - \hat{m}_{ij}$')
mp.ylabel(r'$N \left(m_{ij} - \hat{m}_{ij}\right)$')
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
And another test that the intrinsic scatter is added as expected.
|
# plot difference between true apparent magnitudes and expected apparent
# magnitude given a perfect (i.e., intrinsic-scatter-free) period-luminosity
# relation. this should be the intrinsic scatter, which is Gaussian-
# distributed with mean zero and std dev m_sig_int
eps = np.zeros((n_gal, n_star))
for i in range(n_gal):
eps[i, :] = mu_true[i] + abs_true + s_true * lp_true[i, :] - m_true[i, :]
outs = mp.hist(eps.flatten())
dm_grid = np.linspace(np.min(outs[1]), np.max(outs[1]))
mp.plot(dm_grid, np.exp(-0.5 * (dm_grid/m_sig_int) ** 2) * np.max(outs[0]))
mp.xlabel(r'$m_{ij} - \hat{m}_{ij}$')
mp.ylabel(r'$N \left(m_{ij} - \hat{m}_{ij}\right)$')
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Generalized Least Squares DemoCoding up the [GLS estimator](https://en.wikipedia.org/wiki/Generalized_least_squares) is a little involved, so I've done it for you below. Note that, rather unhelpfully, I've done so in a different order than in the notes. When I get a chance I will re-write. For now, you can simply evaluate the cells and bask in the glory of the fastest inference you will ever do!
|
def gls_fit(n_gal, n_star, mu_hat, mu_hat_sig, m_hat, m_sig_int, m_hat_sig, \
lp_true, priors=None):
# setup
# n_obs is one anchor constraint and one magnitude per Cepheid.
# n_par is one mu per Cepheid host and 2 CPL params. if priors
# are used, we add on n_gal + 2 observations: one prior constraint
# on each host distance modulus and CPL parameter
n_obs = n_gal * n_star + 1
n_par = n_gal + 2
if priors is not None:
n_obs += n_gal + 2
data = np.zeros(n_obs)
design = np.zeros((n_obs, n_par))
cov_inv = np.zeros((n_obs, n_obs))
# anchor
data[0] = mu_hat
design[0, 0] = 1.0
cov_inv[0, 0] = 1.0 / mu_hat_sig ** 2
# Cepheids
k = 1
for i in range(0, n_gal):
for j in range(0, n_star):
data[k] = m_hat[i, j]
design[k, i] = 1.0
design[k, n_gal] = 1.0
design[k, n_gal + 1] = lp_true[i, j]
cov_inv[k, k] = 1.0 / (m_hat_sig ** 2 + m_sig_int ** 2)
k += 1
# and, finally, priors if desired
if priors is not None:
abs_bar, abs_sig, s_bar, s_sig, mu_bar, mu_sig = priors
for i in range(n_gal):
data[k] = mu_bar
design[k, i] = 1.0
cov_inv[k, k] = 1.0 / mu_sig ** 2
k += 1
data[k] = abs_bar
design[k, n_gal] = 1.0
cov_inv[k, k] = 1.0 / abs_sig ** 2
k += 1
data[k] = s_bar
design[k, n_gal + 1] = 1.0
cov_inv[k, k] = 1.0 / s_sig ** 2
k += 1
# fit and return
destci = np.dot(design.transpose(), cov_inv)
pars_cov = np.linalg.inv(np.dot(destci, design))
pars = np.dot(np.dot(pars_cov, destci), data)
res = data - np.dot(design, pars)
dof = n_obs - n_par
chisq_dof = np.dot(res.transpose(), np.dot(cov_inv, res))
return pars, pars_cov, chisq_dof
gls_pars, gls_pars_cov, gls_chisq = gls_fit(n_gal, n_star, mu_hat, mu_hat_sig, m_hat, \
m_sig_int, m_hat_sig, lp_true, \
priors=[abs_bar, abs_sig, s_bar, s_sig, mu_bar, mu_sig])
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
In order to plot the outputs of the GLS fit we could draw a large number of samples from the resulting multivariate Gaussian posterior and pass them to something like [`corner`](https://corner.readthedocs.io/en/latest/); however, as we have analytic results we might as well use those directly. I've coded up something totally hacky here in order to do so. Information on how to draw confidence ellipses can be found in [Dan Coe's note](https://arxiv.org/pdf/0906.4123.pdf).
|
# this is a hacky function designed to transform the analytic GLS outputs
# into a corner.py style triangle plot, containing 1D and 2D marginalized
# posteriors
import scipy.stats as sps
import matplotlib.patches as mpp
def schmorner(par_mean, par_cov, par_true, par_label):
# setup
par_std = np.sqrt(np.diag(par_cov))
x_min = par_mean[0] - 3.5 * par_std[0]
x_max = par_mean[0] + 3.5 * par_std[0]
y_min = par_mean[1] - 3.5 * par_std[1]
y_max = par_mean[1] + 3.5 * par_std[1]
fig, axes = mp.subplots(2, 2)
# 1D marge
x = np.linspace(x_min, x_max, 100)
axes[0, 0].plot(x, sps.norm.pdf(x, par_mean[0], par_std[0]), 'k')
axes[0, 0].axvline(par_true[0])
axes[1, 0].axvline(par_true[0])
axes[0, 0].set_xticklabels([])
axes[0, 0].set_yticklabels([])
axes[0, 0].set_xlim(x_min, x_max)
axes[0, 0].set_title(par_label[0])
axes[0, 0].set_title(par_label[0] + r'$=' + '{:6.2f}'.format(par_mean[0]) + \
r'\pm' + '{:4.2f}'.format(par_std[0]) + r'$')
y = np.linspace(y_min, y_max, 100)
axes[1, 1].plot(y, sps.norm.pdf(y, par_mean[1], par_std[1]), 'k')
axes[1, 0].axhline(par_true[1])
axes[1, 1].axvline(par_true[1])
axes[1, 1].tick_params(labelleft=False)
axes[1, 1].set_xlim(y_min, y_max)
for tick in axes[1, 1].get_xticklabels():
tick.set_rotation(45)
axes[1, 1].set_title(par_label[1] + r'$=' + '{:5.2f}'.format(par_mean[1]) + \
r'\pm' + '{:4.2f}'.format(par_std[1]) + r'$')
# 2D marge
vals, vecs = np.linalg.eig(par_cov)
theta = np.degrees(np.arctan2(*vecs[::-1, 0]))
w, h = 2 * np.sqrt(vals)
ell = mpp.Ellipse(xy=par_mean, width=w, height=h,
angle=theta, color='k')
ell.set_facecolor("none")
axes[1, 0].add_artist(ell)
ell = mpp.Ellipse(xy=par_mean, width=2*w, height=2*h,
angle=theta, color='k')
ell.set_facecolor("none")
axes[1, 0].add_artist(ell)
axes[1, 0].set_xlim(x_min, x_max)
axes[1, 0].set_ylim(y_min, y_max)
for tick in axes[1, 0].get_xticklabels():
tick.set_rotation(45)
for tick in axes[1, 0].get_yticklabels():
tick.set_rotation(45)
axes[1, 0].set_xlabel(par_label[0])
axes[1, 0].set_ylabel(par_label[1])
fig.delaxes(axes[0, 1])
fig.subplots_adjust(hspace=0, wspace=0)
test = schmorner(gls_pars[n_gal:], gls_pars_cov[n_gal:, n_gal:], \
[abs_true, s_true], [r'$M$', r'$s$'])
#
#lazy = npr.multivariate_normal(gls_pars[n_gal:], gls_pars_cov[n_gal:, n_gal:], n_samples)
#fig = corner.corner(samples.T, labels=[r"$M$", r"$s$"],
# show_titles=True, truths=[abs_bar, s_bar])
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Task 3BBelow I've written the majority of a Gibbs sampler to infer the hyper-parameters of the Cepheid PL relation from our simulated sample. One component is missing: drawing from the conditional distribution of the standard absolute magnitude, $M^*$. Please fill it in, using the results of whiteboard/paper Task 3A.
|
def gibbs_sample(n_samples, n_gal, n_star, abs_bar, abs_sig, \
s_bar, s_sig, mu_bar, mu_sig, mu_hat_sig, \
m_sig_int, m_hat_sig, mu_hat, lp_true, m_hat):
# storage
abs_samples = np.zeros(n_samples)
s_samples = np.zeros(n_samples)
mu_samples = np.zeros((n_gal, n_samples))
m_samples = np.zeros((n_gal, n_star, n_samples))
# initialize sampler
abs_samples[0] = abs_bar + npr.randn() * abs_sig
s_samples[0] = s_bar + npr.randn() * s_sig
mu_samples[:, 0] = mu_bar + npr.randn(n_gal) * mu_bar
for i in range(n_gal):
m_samples[i, :, 0] = mu_samples[i, 0] + abs_samples[0] + s_samples[0] * lp_true[i, :]
# sample!
for k in range(1, n_samples):
# sample abs mag
abs_sig_pl = m_sig_int / np.sqrt(n_gal * n_star)
abs_bar_pl = 0.0
for j in range(n_gal):
abs_bar_pl += np.sum(m_samples[j, :, k - 1] - mu_samples[j, k - 1] - s_samples[k - 1] * lp_true[j, :])
abs_bar_pl /= (n_gal * n_star)
abs_std = np.sqrt((abs_sig * abs_sig_pl) ** 2 / (abs_sig ** 2 + abs_sig_pl ** 2))
abs_mean = (abs_sig ** 2 * abs_bar_pl + abs_sig_pl ** 2 * abs_bar) / \
(abs_sig ** 2 + abs_sig_pl ** 2)
abs_samples[k] = abs_mean + npr.randn() * abs_std
# sample slope
s_sig_pl = m_sig_int / np.sqrt(np.sum(lp_true ** 2))
s_bar_pl = 0.0
for j in range(n_gal):
s_bar_pl += np.sum((m_samples[j, :, k - 1] - mu_samples[j, k - 1] - abs_samples[k]) * lp_true[j, :])
s_bar_pl /= np.sum(lp_true ** 2)
s_std = np.sqrt((s_sig * s_sig_pl) ** 2 / (s_sig ** 2 + s_sig_pl ** 2))
s_mean = (s_sig ** 2 * s_bar_pl + s_sig_pl ** 2 * s_bar) / \
(s_sig ** 2 + s_sig_pl ** 2)
s_samples[k] = s_mean + npr.randn() * s_std
# sample apparent magnitudes
for j in range(n_gal):
m_mean_pl = mu_samples[j, k - 1] + abs_samples[k] + s_samples[k] * lp_true[j, :]
m_std = np.sqrt(m_sig_int ** 2 * m_hat_sig ** 2 / (m_sig_int ** 2 + m_hat_sig ** 2))
m_mean = (m_sig_int ** 2 * m_hat[j, :] + m_hat_sig ** 2 * m_mean_pl) / (m_sig_int ** 2 + m_hat_sig ** 2)
m_samples[j, :, k] = m_mean + npr.randn(n_star) * m_std
# sample distance moduli
mu_sig_pl = m_sig_int / np.sqrt(n_star)
mu_bar_pl = np.mean(m_samples[0, :, k] - abs_samples[k] - s_samples[k] * lp_true[0, :])
mu_var = 1.0 / (1.0 / mu_sig ** 2 + 1.0 / mu_hat_sig ** 2 + 1.0 / mu_sig_pl ** 2)
mu_mean = (mu_bar / mu_sig ** 2 + mu_hat / mu_hat_sig ** 2 + mu_bar_pl / mu_sig_pl ** 2) * mu_var
mu_samples[0, k] = mu_mean + npr.randn() * np.sqrt(mu_var)
for j in range(1, n_gal):
mu_sig_pl = m_sig_int / np.sqrt(n_star)
mu_bar_pl = np.mean(m_samples[j, :, k] - abs_samples[k] - s_samples[k] * lp_true[j, :])
mu_std = (mu_sig * mu_sig_pl) ** 2 / (mu_sig ** 2 + mu_sig_pl ** 2)
mu_mean = (mu_sig ** 2 * mu_bar_pl + mu_sig_pl ** 2 * mu_bar) / \
(mu_sig ** 2 + mu_sig_pl ** 2)
mu_samples[j, k] = mu_mean + npr.randn() * mu_std
return (abs_samples, s_samples, mu_samples, m_samples)
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Now let's sample, setting aside the first half of the samples as warmup.
|
all_samples = gibbs_sample(n_samples, n_gal, n_star, abs_bar, abs_sig, \
s_bar, s_sig, mu_bar, mu_sig, mu_hat_sig, \
m_sig_int, m_hat_sig, mu_hat, lp_true, m_hat)
n_warmup = int(n_samples / 2)
g_samples = [samples[n_warmup:] for samples in all_samples]
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Let's make sure that the absolute magnitude is being inferred as expected. First, generate a trace plot of the absolute magnitude samples (the first entry in `g_samples`), overlaying the ground truth. Then print out the mean and standard deviation of the marginalized absolute magnitude posterior. Recall that marginalizing is as simple as throwing away the samples of all other parameters.
|
mp.plot(g_samples[0])
mp.axhline(abs_true)
mp.xlabel('sample')
mp.ylabel(r'$M^*$')
print('Truth {:6.2f}; inferred {:6.2f} +/- {:4.2f}'.format(abs_true, np.mean(g_samples[0]), np.std(g_samples[0])))
|
Truth -30.95; inferred -30.97 +/- 0.02
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Now let's generate some marginalized parameter posteriors (by simply discarding all samples of the latent parameters) using DFM's [`corner`](https://corner.readthedocs.io/en/latest/) package. Note the near identical nature of this plot to the `schmorner` plot we generated above.
|
import corner
samples = np.stack((g_samples[0], g_samples[1]))
fig = corner.corner(samples.T, labels=[r"$M^*$", r"$s$"],
show_titles=True, truths=[abs_true, s_true])
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Task 4The final task is to write a [Stan model](https://pystan.readthedocs.io/en/latest/getting_started.html) to infer the parameters of the period-luminosity relation. I've coded up the other two blocks required (`data` and `parameters`), so all that is required is for you to write the joint posterior (factorized into its individual components) in Stan's sampling-statement-based syntax. Essentially all you need are Gaussian sampling statements (`abs_true ~ normal(abs_bar, abs_sig);`) and for loops (`for(i in 1: n_gal){...}`).When you evaluate this cell, Stan will translate your model into `c++` code and compile it. We will then pickle the compiled model so you can re-use it rapidly without recompiling. To do so, please set `recompile = False` in the notebook.
|
import sys
import pystan as ps
import pickle
stan_code = """
data {
int<lower=0> n_gal;
int<lower=0> n_star;
real mu_hat;
real mu_hat_sig;
real m_hat[n_gal, n_star];
real m_hat_sig;
real m_sig_int;
real lp_true[n_gal, n_star];
real abs_bar;
real abs_sig;
real s_bar;
real s_sig;
real mu_bar;
real mu_sig;
}
parameters {
real mu_true[n_gal];
real m_true[n_gal, n_star];
real abs_true;
real s_true;
}
model {
// priors
abs_true ~ normal(abs_bar, abs_sig);
s_true ~ normal(s_bar, s_sig);
mu_true ~ normal(mu_bar, mu_sig);
// whatevers
for(i in 1: n_gal){
for(j in 1: n_star){
m_true[i, j] ~ normal(mu_true[i] + abs_true + s_true * lp_true[i, j], m_sig_int);
}
}
// likelihoods
mu_hat ~ normal(mu_true[1], mu_hat_sig);
for(i in 1: n_gal){
for(j in 1: n_star){
m_hat[i, j] ~ normal(m_true[i, j], m_hat_sig);
}
}
}
"""
n_samples_stan = 5000
recompile = True
pkl_fname = 'bhms_stan_model_v{:d}p{:d}p{:d}.pkl'.format(sys.version_info[0], \
sys.version_info[1], \
sys.version_info[2])
if recompile:
stan_model = ps.StanModel(model_code=stan_code)
with open(pkl_fname, 'wb') as f:
pickle.dump(stan_model, f)
else:
try:
with open(pkl_fname, 'rb') as f:
stan_model = pickle.load(f)
except EnvironmentError:
print('ERROR: pickled Stan model (' + pkl_fname + ') not found. ' + \
'Please set recompile = True')
|
INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_ab41b39e55c2f57c74acf30e86ea4ea5 NOW.
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Now let's sample...
|
stan_data = {'n_gal': n_gal, 'n_star': n_star, 'mu_hat': mu_hat, 'mu_hat_sig': mu_hat_sig, \
'm_hat': m_hat, 'm_hat_sig': m_hat_sig, 'm_sig_int': m_sig_int, 'lp_true': lp_true, \
'abs_bar': abs_bar, 'abs_sig': abs_sig, 's_bar': s_bar, 's_sig': s_sig, \
'mu_bar': mu_bar, 'mu_sig': mu_sig}
fit = stan_model.sampling(data=stan_data, iter=n_samples_stan, chains=4)
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
... print out Stan's posterior summary (note this is for _all_ parameters)...
|
samples = fit.extract(permuted=True)
print(fit)
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
... and plot the marginalized posterior of the PL parameters, as with the Gibbs sampler.
|
c_samples = np.stack((samples['abs_true'], samples['s_true']))
fig = corner.corner(c_samples.T, labels=[r"$M^*$", r"$s$"],
show_titles=True, truths=[abs_true, s_true])
|
_____no_output_____
|
MIT
|
bhms.ipynb
|
sfeeney/bhm_lecture
|
Detecting Loops in Linked ListsIn this notebook, you'll implement a function that detects if a loop exists in a linked list. The way we'll do this is by having two pointers, called "runners", moving through the list at different rates. Typically we have a "slow" runner which moves at one node per step and a "fast" runner that moves at two nodes per step.If a loop exists in the list, the fast runner will eventually move behind the slow runner as it moves to the beginning of the loop. Eventually it will catch up to the slow runner and both runners will be pointing to the same node at the same time. If this happens then you know there is a loop in the linked list. Below is an example where we have a slow runner (the green arrow) and a fast runner (the red arrow).
|
class Node:
def __init__(self, value):
self.value = value
self.next = None
class LinkedList:
def __init__(self, init_list=None):
self.head = None
if init_list:
for value in init_list:
self.append(value)
def append(self, value):
if self.head is None:
self.head = Node(value)
return
# Move to the tail (the last node)
node = self.head
while node.next:
node = node.next
node.next = Node(value)
return
def __iter__(self):
node = self.head
while node:
yield node.value
node = node.next
def __repr__(self):
return str([i for i in self])
list_with_loop = LinkedList([2, -1, 3, 0, 5])
# Creating a loop where the last node points back to the second node
loop_start = list_with_loop.head.next
node = list_with_loop.head
while node.next:
node = node.next
node.next = loop_start
# You will encouter the unlimited loop
# Click on stop
# Then right click on `clear outpit`
for i in list_with_loop:
print(i)
|
_____no_output_____
|
MIT
|
2/1-3/linked_lists/Detecting Loops.ipynb
|
ZacksAmber/Udacity-Data-Structure-Algorithms
|
Write the function definition here**Exercise:** Given a linked list, implement a function `iscircular` that returns `True` if a loop exists in the list and `False` otherwise.
|
def iscircular(linked_list):
"""
Determine whether the Linked List is circular or not
Args:
linked_list(obj): Linked List to be checked
Returns:
bool: Return True if the linked list is circular, return False otherwise
"""
# TODO: Write function to check if linked list is circular
if linked_list is None:
return False
slow, fast = linked_list.head, linked_list.head
while fast and fast.next:
slow, fast = slow.next, fast.next.next
if slow == fast:
return True
return False
|
_____no_output_____
|
MIT
|
2/1-3/linked_lists/Detecting Loops.ipynb
|
ZacksAmber/Udacity-Data-Structure-Algorithms
|
Let's test your function
|
iscircular(list_with_loop)
# Test Cases
# Create another circular linked list
small_loop = LinkedList([0])
small_loop.head.next = small_loop.head
print ("Pass" if iscircular(list_with_loop) else "Fail") # Pass
print ("Pass" if iscircular(LinkedList([-4, 7, 2, 5, -1])) else "Fail") # Fail
print ("Pass" if iscircular(LinkedList([1])) else "Fail") # Fail
print ("Pass" if iscircular(small_loop) else "Fail") # Pass
print ("Pass" if iscircular(LinkedList([])) else "Fail") # Fail
|
Pass
Fail
Fail
Pass
Fail
|
MIT
|
2/1-3/linked_lists/Detecting Loops.ipynb
|
ZacksAmber/Udacity-Data-Structure-Algorithms
|
Normalize> Data normalization methods.
|
#hide
from nbdev.showdoc import *
#export
def simple_normalize(data, method='minmax', target_column='RATING'):
zscore = lambda x: (x - x.mean()) / x.std()
minmax = lambda x: (x - x.min()) / (x.max() - x.min())
if method=='minmax':
norm = data.groupby('USERID')[target_column].transform(minmax)
elif method=='zscore':
norm = data.groupby('USERID')[target_column].transform(zscore)
data.loc[:,target_column] = norm
return data
#hide
!pip install -q watermark
%reload_ext watermark
%watermark -a "Sparsh A." -m -iv -u -t -d
|
Author: Sparsh A.
Last updated: 2021-12-18 08:35:26
Compiler : GCC 7.5.0
OS : Linux
Release : 5.4.104+
Machine : x86_64
Processor : x86_64
CPU cores : 2
Architecture: 64bit
IPython: 5.5.0
|
Apache-2.0
|
nbs/utils/utils.normalize.ipynb
|
sparsh-ai/recohut
|
Copyright 2020 The Google AI Language Team AuthorsLicensed under the Apache License, Version 2.0 (the "License");
|
# Copyright 2019 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
|
_____no_output_____
|
Apache-2.0
|
notebooks/sqa_predictions.ipynb
|
aniket371/tapas
|
Running a Tapas fine-tuned checkpoint---This notebook shows how to load and make predictions with TAPAS model, which was introduced in the paper: [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) Clone and install the repository First, let's install the code.
|
! pip install tapas-table-parsing
|
Collecting tapas-table-parsing
Downloading tapas_table_parsing-0.0.1.dev0-py3-none-any.whl (195 kB)
[?25l
[K |█▊ | 10 kB 22.3 MB/s eta 0:00:01
[K |███▍ | 20 kB 28.7 MB/s eta 0:00:01
[K |█████ | 30 kB 16.4 MB/s eta 0:00:01
[K |██████▊ | 40 kB 11.4 MB/s eta 0:00:01
[K |████████▍ | 51 kB 5.7 MB/s eta 0:00:01
[K |██████████ | 61 kB 6.7 MB/s eta 0:00:01
[K |███████████▊ | 71 kB 7.3 MB/s eta 0:00:01
[K |█████████████▍ | 81 kB 5.6 MB/s eta 0:00:01
[K |███████████████ | 92 kB 6.2 MB/s eta 0:00:01
[K |████████████████▊ | 102 kB 6.8 MB/s eta 0:00:01
[K |██████████████████▍ | 112 kB 6.8 MB/s eta 0:00:01
[K |████████████████████ | 122 kB 6.8 MB/s eta 0:00:01
[K |█████████████████████▉ | 133 kB 6.8 MB/s eta 0:00:01
[K |███████████████████████▌ | 143 kB 6.8 MB/s eta 0:00:01
[K |█████████████████████████▏ | 153 kB 6.8 MB/s eta 0:00:01
[K |██████████████████████████▉ | 163 kB 6.8 MB/s eta 0:00:01
[K |████████████████████████████▌ | 174 kB 6.8 MB/s eta 0:00:01
[K |██████████████████████████████▏ | 184 kB 6.8 MB/s eta 0:00:01
[K |███████████████████████████████▉| 194 kB 6.8 MB/s eta 0:00:01
[K |████████████████████████████████| 195 kB 6.8 MB/s
[?25hCollecting frozendict==1.2
Downloading frozendict-1.2.tar.gz (2.6 kB)
Collecting pandas~=1.0.0
Downloading pandas-1.0.5-cp37-cp37m-manylinux1_x86_64.whl (10.1 MB)
[K |████████████████████████████████| 10.1 MB 51.1 MB/s
[?25hCollecting tensorflow-probability==0.10.1
Downloading tensorflow_probability-0.10.1-py2.py3-none-any.whl (3.5 MB)
[K |████████████████████████████████| 3.5 MB 43.8 MB/s
[?25hCollecting nltk~=3.5
Downloading nltk-3.7-py3-none-any.whl (1.5 MB)
[K |████████████████████████████████| 1.5 MB 47.9 MB/s
[?25hCollecting scikit-learn~=0.22.1
Downloading scikit_learn-0.22.2.post1-cp37-cp37m-manylinux1_x86_64.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 36.9 MB/s
[?25hCollecting kaggle<1.5.8
Downloading kaggle-1.5.6.tar.gz (58 kB)
[K |████████████████████████████████| 58 kB 5.4 MB/s
[?25hCollecting tf-models-official~=2.2.0
Downloading tf_models_official-2.2.2-py2.py3-none-any.whl (711 kB)
[K |████████████████████████████████| 711 kB 53.0 MB/s
[?25hCollecting tensorflow~=2.2.0
Downloading tensorflow-2.2.3-cp37-cp37m-manylinux2010_x86_64.whl (516.4 MB)
[K |████████████████████████████████| 516.4 MB 17 kB/s
[?25hCollecting apache-beam[gcp]==2.20.0
Downloading apache_beam-2.20.0-cp37-cp37m-manylinux1_x86_64.whl (3.5 MB)
[K |████████████████████████████████| 3.5 MB 45.4 MB/s
[?25hCollecting tf-slim~=1.1.0
Downloading tf_slim-1.1.0-py2.py3-none-any.whl (352 kB)
[K |████████████████████████████████| 352 kB 56.0 MB/s
[?25hRequirement already satisfied: future<1.0.0,>=0.16.0 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (0.16.0)
Requirement already satisfied: pytz>=2018.3 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (2018.9)
Requirement already satisfied: numpy<2,>=1.14.3 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.21.5)
Collecting fastavro<0.22,>=0.21.4
Downloading fastavro-0.21.24-cp37-cp37m-manylinux1_x86_64.whl (1.2 MB)
[K |████████████████████████████████| 1.2 MB 36.6 MB/s
[?25hRequirement already satisfied: protobuf<4,>=3.5.0.post1 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (3.17.3)
Collecting dill<0.3.2,>=0.3.1.1
Downloading dill-0.3.1.1.tar.gz (151 kB)
[K |████████████████████████████████| 151 kB 42.9 MB/s
[?25hCollecting httplib2<=0.12.0,>=0.8
Downloading httplib2-0.12.0.tar.gz (218 kB)
[K |████████████████████████████████| 218 kB 50.0 MB/s
[?25hCollecting oauth2client<4,>=2.0.1
Downloading oauth2client-3.0.0.tar.gz (77 kB)
[K |████████████████████████████████| 77 kB 5.8 MB/s
[?25hCollecting mock<3.0.0,>=1.0.1
Downloading mock-2.0.0-py2.py3-none-any.whl (56 kB)
[K |████████████████████████████████| 56 kB 4.7 MB/s
[?25hCollecting pymongo<4.0.0,>=3.8.0
Downloading pymongo-3.12.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (508 kB)
[K |████████████████████████████████| 508 kB 31.5 MB/s
[?25hCollecting hdfs<3.0.0,>=2.1.0
Downloading hdfs-2.7.0-py3-none-any.whl (34 kB)
Collecting typing-extensions<3.8.0,>=3.7.0
Downloading typing_extensions-3.7.4.3-py3-none-any.whl (22 kB)
Collecting avro-python3!=1.9.2,<1.10.0,>=1.8.1
Downloading avro-python3-1.9.2.1.tar.gz (37 kB)
Requirement already satisfied: crcmod<2.0,>=1.7 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.7)
Requirement already satisfied: python-dateutil<3,>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (2.8.2)
Collecting pyarrow<0.17.0,>=0.15.1
Downloading pyarrow-0.16.0-cp37-cp37m-manylinux2014_x86_64.whl (63.1 MB)
[K |████████████████████████████████| 63.1 MB 35 kB/s
[?25hRequirement already satisfied: grpcio<2,>=1.12.1 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.44.0)
Requirement already satisfied: pydot<2,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.3.0)
Collecting google-cloud-dlp<=0.13.0,>=0.12.0
Downloading google_cloud_dlp-0.13.0-py2.py3-none-any.whl (151 kB)
[K |████████████████████████████████| 151 kB 51.1 MB/s
[?25hRequirement already satisfied: google-cloud-core<2,>=0.28.1 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.0.3)
Collecting google-cloud-bigtable<1.1.0,>=0.31.1
Downloading google_cloud_bigtable-1.0.0-py2.py3-none-any.whl (232 kB)
[K |████████████████████████████████| 232 kB 55.5 MB/s
[?25hCollecting google-cloud-language<2,>=1.3.0
Downloading google_cloud_language-1.3.0-py2.py3-none-any.whl (83 kB)
[K |████████████████████████████████| 83 kB 1.7 MB/s
[?25hCollecting google-cloud-vision<0.43.0,>=0.38.0
Downloading google_cloud_vision-0.42.0-py2.py3-none-any.whl (435 kB)
[K |████████████████████████████████| 435 kB 52.1 MB/s
[?25hRequirement already satisfied: google-cloud-bigquery<=1.24.0,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.21.0)
Collecting grpcio-gcp<1,>=0.2.2
Downloading grpcio_gcp-0.2.2-py2.py3-none-any.whl (9.4 kB)
Collecting google-cloud-spanner<1.14.0,>=1.13.0
Downloading google_cloud_spanner-1.13.0-py2.py3-none-any.whl (212 kB)
[K |████████████████████████████████| 212 kB 68.0 MB/s
[?25hCollecting google-cloud-datastore<1.8.0,>=1.7.1
Downloading google_cloud_datastore-1.7.4-py2.py3-none-any.whl (82 kB)
[K |████████████████████████████████| 82 kB 1.1 MB/s
[?25hCollecting cachetools<4,>=3.1.0
Downloading cachetools-3.1.1-py2.py3-none-any.whl (11 kB)
Collecting google-cloud-videointelligence<1.14.0,>=1.8.0
Downloading google_cloud_videointelligence-1.13.0-py2.py3-none-any.whl (177 kB)
[K |████████████████████████████████| 177 kB 64.9 MB/s
[?25hCollecting google-cloud-pubsub<1.1.0,>=0.39.0
Downloading google_cloud_pubsub-1.0.2-py2.py3-none-any.whl (118 kB)
[K |████████████████████████████████| 118 kB 68.4 MB/s
[?25hCollecting google-apitools<0.5.29,>=0.5.28
Downloading google-apitools-0.5.28.tar.gz (172 kB)
[K |████████████████████████████████| 172 kB 65.3 MB/s
[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability==0.10.1->tapas-table-parsing) (4.4.2)
Requirement already satisfied: gast>=0.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability==0.10.1->tapas-table-parsing) (0.5.3)
Requirement already satisfied: cloudpickle==1.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability==0.10.1->tapas-table-parsing) (1.3.0)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-probability==0.10.1->tapas-table-parsing) (1.15.0)
Collecting fasteners>=0.14
Downloading fasteners-0.17.3-py3-none-any.whl (18 kB)
Requirement already satisfied: google-resumable-media!=0.4.0,<0.5.0dev,>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigquery<=1.24.0,>=1.6.0->apache-beam[gcp]==2.20.0->tapas-table-parsing) (0.4.1)
Requirement already satisfied: google-api-core[grpc]<2.0.0dev,>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.26.3)
Collecting grpc-google-iam-v1<0.13dev,>=0.12.3
Downloading grpc-google-iam-v1-0.12.3.tar.gz (13 kB)
Requirement already satisfied: google-auth<2.0dev,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.35.0)
Requirement already satisfied: setuptools>=40.3.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (57.4.0)
Requirement already satisfied: packaging>=14.3 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (21.3)
Requirement already satisfied: requests<3.0.0dev,>=2.18.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (2.23.0)
Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (1.56.0)
Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (4.8)
Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (0.2.8)
Requirement already satisfied: docopt in /usr/local/lib/python3.7/dist-packages (from hdfs<3.0.0,>=2.1.0->apache-beam[gcp]==2.20.0->tapas-table-parsing) (0.6.2)
Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from kaggle<1.5.8->tapas-table-parsing) (1.24.3)
Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle<1.5.8->tapas-table-parsing) (2021.10.8)
Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle<1.5.8->tapas-table-parsing) (4.63.0)
Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle<1.5.8->tapas-table-parsing) (6.1.1)
Collecting pbr>=0.11
Downloading pbr-5.8.1-py2.py3-none-any.whl (113 kB)
[K |████████████████████████████████| 113 kB 63.8 MB/s
[?25hRequirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from nltk~=3.5->tapas-table-parsing) (7.1.2)
Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from nltk~=3.5->tapas-table-parsing) (1.1.0)
Collecting regex>=2021.8.3
Downloading regex-2022.3.15-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (749 kB)
[K |████████████████████████████████| 749 kB 45.4 MB/s
[?25hRequirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.7/dist-packages (from oauth2client<4,>=2.0.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (0.4.8)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=14.3->google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (3.0.7)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (2.10)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0dev,>=2.18.0->google-api-core[grpc]<2.0.0dev,>=1.14.0->google-cloud-bigtable<1.1.0,>=0.31.1->apache-beam[gcp]==2.20.0->tapas-table-parsing) (3.0.4)
Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn~=0.22.1->tapas-table-parsing) (1.4.1)
Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (1.6.3)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (1.1.0)
Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (1.14.0)
Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (3.3.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (0.37.1)
Collecting tensorboard<2.3.0,>=2.2.0
Downloading tensorboard-2.2.2-py3-none-any.whl (3.0 MB)
[K |████████████████████████████████| 3.0 MB 39.4 MB/s
[?25hCollecting h5py<2.11.0,>=2.10.0
Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)
[K |████████████████████████████████| 2.9 MB 44.0 MB/s
[?25hRequirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (0.2.0)
Collecting numpy<2,>=1.14.3
Downloading numpy-1.18.5-cp37-cp37m-manylinux1_x86_64.whl (20.1 MB)
[K |████████████████████████████████| 20.1 MB 82.3 MB/s
[?25hCollecting tensorflow-estimator<2.3.0,>=2.2.0
Downloading tensorflow_estimator-2.2.0-py2.py3-none-any.whl (454 kB)
[K |████████████████████████████████| 454 kB 65.6 MB/s
[?25hCollecting gast>=0.3.2
Downloading gast-0.3.3-py2.py3-none-any.whl (9.7 kB)
Requirement already satisfied: keras-preprocessing>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (1.1.2)
Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow~=2.2.0->tapas-table-parsing) (1.0.0)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (0.4.6)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (1.8.1)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (1.0.1)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (3.3.6)
Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (1.3.1)
Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (4.11.3)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (3.7.0)
Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.3.0,>=2.2.0->tensorflow~=2.2.0->tapas-table-parsing) (3.2.0)
Collecting mlperf-compliance==0.0.10
Downloading mlperf_compliance-0.0.10-py3-none-any.whl (24 kB)
Requirement already satisfied: tensorflow-hub>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (0.12.0)
Collecting sentencepiece
Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
[K |████████████████████████████████| 1.2 MB 37.7 MB/s
[?25hCollecting typing==3.7.4.1
Downloading typing-3.7.4.1-py3-none-any.whl (25 kB)
Requirement already satisfied: gin-config in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (0.5.0)
Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (3.13)
Collecting opencv-python-headless
Downloading opencv_python_headless-4.5.5.64-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (47.8 MB)
[K |████████████████████████████████| 47.8 MB 49 kB/s
[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (3.2.2)
Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (0.29.28)
Requirement already satisfied: tensorflow-datasets in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (4.0.1)
Requirement already satisfied: psutil>=5.4.3 in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (5.4.8)
Collecting tensorflow-model-optimization>=0.2.1
Downloading tensorflow_model_optimization-0.7.2-py2.py3-none-any.whl (237 kB)
[K |████████████████████████████████| 237 kB 64.3 MB/s
[?25hCollecting py-cpuinfo>=3.3.0
Downloading py-cpuinfo-8.0.0.tar.gz (99 kB)
[K |████████████████████████████████| 99 kB 9.3 MB/s
[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (7.1.2)
Requirement already satisfied: google-api-python-client>=1.6.7 in /usr/local/lib/python3.7/dist-packages (from tf-models-official~=2.2.0->tapas-table-parsing) (1.12.11)
Collecting dataclasses
Downloading dataclasses-0.6-py3-none-any.whl (14 kB)
Collecting tensorflow-addons
Downloading tensorflow_addons-0.16.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)
[K |████████████████████████████████| 1.1 MB 37.8 MB/s
[?25hCollecting google-api-python-client>=1.6.7
Downloading google_api_python_client-2.42.0-py2.py3-none-any.whl (8.3 MB)
[K |████████████████████████████████| 8.3 MB 64.3 MB/s
[?25h Downloading google_api_python_client-2.41.0-py2.py3-none-any.whl (8.3 MB)
[K |████████████████████████████████| 8.3 MB 21.1 MB/s
[?25h Downloading google_api_python_client-2.40.0-py2.py3-none-any.whl (8.2 MB)
[K |████████████████████████████████| 8.2 MB 44.2 MB/s
[?25h Downloading google_api_python_client-2.39.0-py2.py3-none-any.whl (8.2 MB)
[K |████████████████████████████████| 8.2 MB 38.0 MB/s
[?25h Downloading google_api_python_client-2.38.0-py2.py3-none-any.whl (8.2 MB)
[K |████████████████████████████████| 8.2 MB 41.1 MB/s
[?25h Downloading google_api_python_client-2.37.0-py2.py3-none-any.whl (8.1 MB)
[K |████████████████████████████████| 8.1 MB 30.0 MB/s
[?25h Downloading google_api_python_client-2.36.0-py2.py3-none-any.whl (8.0 MB)
[K |████████████████████████████████| 8.0 MB 50.5 MB/s
[?25h Downloading google_api_python_client-2.35.0-py2.py3-none-any.whl (8.0 MB)
[K |████████████████████████████████| 8.0 MB 35.1 MB/s
[?25h Downloading google_api_python_client-2.34.0-py2.py3-none-any.whl (7.9 MB)
[K |████████████████████████████████| 7.9 MB 50.6 MB/s
[?25h Downloading google_api_python_client-2.33.0-py2.py3-none-any.whl (7.9 MB)
[K |████████████████████████████████| 7.9 MB 26.7 MB/s
[?25h Downloading google_api_python_client-2.32.0-py2.py3-none-any.whl (7.8 MB)
[K |████████████████████████████████| 7.8 MB 50.7 MB/s
[?25h Downloading google_api_python_client-2.31.0-py2.py3-none-any.whl (7.8 MB)
[K |████████████████████████████████| 7.8 MB 31.8 MB/s
[?25h Downloading google_api_python_client-2.30.0-py2.py3-none-any.whl (7.8 MB)
[K |████████████████████████████████| 7.8 MB 25.6 MB/s
[?25h Downloading google_api_python_client-2.29.0-py2.py3-none-any.whl (7.7 MB)
[K |████████████████████████████████| 7.7 MB 45.6 MB/s
[?25h Downloading google_api_python_client-2.28.0-py2.py3-none-any.whl (7.7 MB)
[K |████████████████████████████████| 7.7 MB 32.2 MB/s
[?25h Downloading google_api_python_client-2.27.0-py2.py3-none-any.whl (7.7 MB)
[K |████████████████████████████████| 7.7 MB 37.2 MB/s
[?25h Downloading google_api_python_client-2.26.1-py2.py3-none-any.whl (7.6 MB)
[K |████████████████████████████████| 7.6 MB 33.4 MB/s
[?25h Downloading google_api_python_client-2.26.0-py2.py3-none-any.whl (7.6 MB)
[K |████████████████████████████████| 7.6 MB 19.4 MB/s
[?25h Downloading google_api_python_client-2.25.0-py2.py3-none-any.whl (7.5 MB)
[K |████████████████████████████████| 7.5 MB 41.9 MB/s
[?25h Downloading google_api_python_client-2.24.0-py2.py3-none-any.whl (7.5 MB)
[K |████████████████████████████████| 7.5 MB 9.5 MB/s
[?25h Downloading google_api_python_client-2.23.0-py2.py3-none-any.whl (7.5 MB)
[K |████████████████████████████████| 7.5 MB 11.0 MB/s
[?25h Downloading google_api_python_client-2.22.0-py2.py3-none-any.whl (7.5 MB)
[K |████████████████████████████████| 7.5 MB 8.1 MB/s
[?25h Downloading google_api_python_client-2.21.0-py2.py3-none-any.whl (7.5 MB)
[K |████████████████████████████████| 7.5 MB 37.5 MB/s
[?25h Downloading google_api_python_client-2.20.0-py2.py3-none-any.whl (7.4 MB)
[K |████████████████████████████████| 7.4 MB 29.5 MB/s
[?25h Downloading google_api_python_client-2.19.1-py2.py3-none-any.whl (7.4 MB)
[K |████████████████████████████████| 7.4 MB 6.7 MB/s
[?25h Downloading google_api_python_client-2.19.0-py2.py3-none-any.whl (7.4 MB)
[K |████████████████████████████████| 7.4 MB 31.1 MB/s
[?25h Downloading google_api_python_client-2.18.0-py2.py3-none-any.whl (7.4 MB)
[K |████████████████████████████████| 7.4 MB 30.6 MB/s
[?25h Downloading google_api_python_client-2.17.0-py2.py3-none-any.whl (7.3 MB)
[K |████████████████████████████████| 7.3 MB 21.9 MB/s
[?25h Downloading google_api_python_client-2.16.0-py2.py3-none-any.whl (7.3 MB)
[K |████████████████████████████████| 7.3 MB 27.5 MB/s
[?25h Downloading google_api_python_client-2.15.0-py2.py3-none-any.whl (7.2 MB)
[K |████████████████████████████████| 7.2 MB 19.3 MB/s
[?25h Downloading google_api_python_client-2.14.1-py2.py3-none-any.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 25.4 MB/s
[?25h Downloading google_api_python_client-2.14.0-py2.py3-none-any.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 22.6 MB/s
[?25h Downloading google_api_python_client-2.13.0-py2.py3-none-any.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 16.9 MB/s
[?25h Downloading google_api_python_client-2.12.0-py2.py3-none-any.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 22.3 MB/s
[?25h Downloading google_api_python_client-2.11.0-py2.py3-none-any.whl (7.0 MB)
[K |████████████████████████████████| 7.0 MB 5.1 MB/s
[?25h Downloading google_api_python_client-2.10.0-py2.py3-none-any.whl (7.0 MB)
[K |████████████████████████████████| 7.0 MB 26.8 MB/s
[?25h Downloading google_api_python_client-2.9.0-py2.py3-none-any.whl (7.0 MB)
[K |████████████████████████████████| 7.0 MB 19.7 MB/s
[?25h Downloading google_api_python_client-2.8.0-py2.py3-none-any.whl (7.0 MB)
[K |████████████████████████████████| 7.0 MB 24.8 MB/s
[?25h Downloading google_api_python_client-2.7.0-py2.py3-none-any.whl (7.3 MB)
[K |████████████████████████████████| 7.3 MB 19.0 MB/s
[?25h Downloading google_api_python_client-2.6.0-py2.py3-none-any.whl (7.2 MB)
[K |████████████████████████████████| 7.2 MB 34.9 MB/s
[?25h Downloading google_api_python_client-2.5.0-py2.py3-none-any.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 17.7 MB/s
[?25h Downloading google_api_python_client-2.4.0-py2.py3-none-any.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 31.2 MB/s
[?25h Downloading google_api_python_client-2.3.0-py2.py3-none-any.whl (7.1 MB)
[K |████████████████████████████████| 7.1 MB 37.4 MB/s
[?25h Downloading google_api_python_client-2.2.0-py2.py3-none-any.whl (7.0 MB)
[K |████████████████████████████████| 7.0 MB 6.2 MB/s
[?25h Downloading google_api_python_client-2.1.0-py2.py3-none-any.whl (6.6 MB)
[K |████████████████████████████████| 6.6 MB 14.4 MB/s
[?25h Downloading google_api_python_client-2.0.2-py2.py3-none-any.whl (6.5 MB)
[K |████████████████████████████████| 6.5 MB 37.7 MB/s
[?25h Downloading google_api_python_client-1.12.10-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 119 kB/s
[?25h Downloading google_api_python_client-1.12.8-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 785 bytes/s
[?25h Downloading google_api_python_client-1.12.7-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 29 kB/s
[?25h Downloading google_api_python_client-1.12.6-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 27 kB/s
[?25h Downloading google_api_python_client-1.12.5-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 7.5 MB/s
[?25h Downloading google_api_python_client-1.12.4-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 7.1 MB/s
[?25h Downloading google_api_python_client-1.12.3-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 5.8 MB/s
[?25h Downloading google_api_python_client-1.12.2-py2.py3-none-any.whl (61 kB)
[K |████████████████████████████████| 61 kB 8.4 MB/s
[?25hRequirement already satisfied: google-auth-httplib2>=0.0.3 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official~=2.2.0->tapas-table-parsing) (0.0.4)
Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from google-api-python-client>=1.6.7->tf-models-official~=2.2.0->tapas-table-parsing) (3.0.1)
Requirement already satisfied: dm-tree~=0.1.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow-model-optimization>=0.2.1->tf-models-official~=2.2.0->tapas-table-parsing) (0.1.6)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->tf-models-official~=2.2.0->tapas-table-parsing) (1.4.0)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->tf-models-official~=2.2.0->tapas-table-parsing) (0.11.0)
Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle<1.5.8->tapas-table-parsing) (1.3)
Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.7/dist-packages (from tensorflow-addons->tf-models-official~=2.2.0->tapas-table-parsing) (2.7.1)
Requirement already satisfied: importlib-resources in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official~=2.2.0->tapas-table-parsing) (5.4.0)
Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official~=2.2.0->tapas-table-parsing) (1.7.0)
Requirement already satisfied: promise in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official~=2.2.0->tapas-table-parsing) (2.3)
Requirement already satisfied: attrs>=18.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-datasets->tf-models-official~=2.2.0->tapas-table-parsing) (21.4.0)
Building wheels for collected packages: frozendict, avro-python3, dill, google-apitools, grpc-google-iam-v1, httplib2, kaggle, oauth2client, py-cpuinfo
Building wheel for frozendict (setup.py) ... [?25l[?25hdone
Created wheel for frozendict: filename=frozendict-1.2-py3-none-any.whl size=3166 sha256=bcbf7ecdf36cf16604986862f798d3cfd039a27a02d608e86236c97dac08c3ae
Stored in directory: /root/.cache/pip/wheels/68/17/69/ac196dd181e620bba5fae5488e4fd6366a7316dce13cf88776
Building wheel for avro-python3 (setup.py) ... [?25l[?25hdone
Created wheel for avro-python3: filename=avro_python3-1.9.2.1-py3-none-any.whl size=43513 sha256=8d5079abbdcb60a53a8929f07491be91b469e9ca1e0b266eb0f868e065147dae
Stored in directory: /root/.cache/pip/wheels/bc/49/5f/fdb5b9d85055c478213e0158ac122b596816149a02d82e0ab1
Building wheel for dill (setup.py) ... [?25l[?25hdone
Created wheel for dill: filename=dill-0.3.1.1-py3-none-any.whl size=78544 sha256=5847f08d96cd5f1809473da81ce0e7cea3badef87bbcc5e8f88f7136bb233a9c
Stored in directory: /root/.cache/pip/wheels/a4/61/fd/c57e374e580aa78a45ed78d5859b3a44436af17e22ca53284f
Building wheel for google-apitools (setup.py) ... [?25l[?25hdone
Created wheel for google-apitools: filename=google_apitools-0.5.28-py3-none-any.whl size=130109 sha256=3a5edaac514084485549d713c595af9a20d638189d5ab3dfa0107675ce6c2937
Stored in directory: /root/.cache/pip/wheels/34/3b/69/ecd8e6ae89d9d71102a58962c29faa7a9467ba45f99f205920
Building wheel for grpc-google-iam-v1 (setup.py) ... [?25l[?25hdone
Created wheel for grpc-google-iam-v1: filename=grpc_google_iam_v1-0.12.3-py3-none-any.whl size=18515 sha256=c6a155cf0d184085c4d718e1e3c19356ba32b9f7c29d3884f6de71f8d14a6387
Stored in directory: /root/.cache/pip/wheels/b9/ee/67/2e444183030cb8d31ce8b34cee34a7afdbd3ba5959ea846380
Building wheel for httplib2 (setup.py) ... [?25l[?25hdone
Created wheel for httplib2: filename=httplib2-0.12.0-py3-none-any.whl size=93465 sha256=e1e718e4ceca2290ca872bd2434defee84953402a8baad2e2b183a115bb6b901
Stored in directory: /root/.cache/pip/wheels/0d/e7/b6/0dd30343ceca921cfbd91f355041bd9c69e0f40b49f25b7b8a
Building wheel for kaggle (setup.py) ... [?25l[?25hdone
Created wheel for kaggle: filename=kaggle-1.5.6-py3-none-any.whl size=72858 sha256=f911a59bdadc590e7f089c41c23f24c49e3d2d586bbefe73925d026b7989d7fc
Stored in directory: /root/.cache/pip/wheels/aa/e7/e7/eb3c3d514c33294d77ddd5a856bdd58dc9c1fabbed59a02a2b
Building wheel for oauth2client (setup.py) ... [?25l[?25hdone
Created wheel for oauth2client: filename=oauth2client-3.0.0-py3-none-any.whl size=106375 sha256=a0b226e54e128315e6205fc9380270bca443fc3e1bac6e135e51a4cd24bb3622
Stored in directory: /root/.cache/pip/wheels/86/73/7a/3b3f76a2142176605ff38fbca574327962c71e25a43197a4c1
Building wheel for py-cpuinfo (setup.py) ... [?25l[?25hdone
Created wheel for py-cpuinfo: filename=py_cpuinfo-8.0.0-py3-none-any.whl size=22257 sha256=113156ecdb59f6181b20ec42ed7d3373f3b3be32ec31144ee5cb7efd4caca5f3
Stored in directory: /root/.cache/pip/wheels/d2/f1/1f/041add21dc9c4220157f1bd2bd6afe1f1a49524c3396b94401
Successfully built frozendict avro-python3 dill google-apitools grpc-google-iam-v1 httplib2 kaggle oauth2client py-cpuinfo
Installing collected packages: typing-extensions, cachetools, pbr, numpy, httplib2, grpcio-gcp, tensorflow-estimator, tensorboard, pymongo, pyarrow, oauth2client, mock, hdfs, h5py, grpc-google-iam-v1, gast, fasteners, fastavro, dill, avro-python3, typing, tensorflow-model-optimization, tensorflow-addons, tensorflow, sentencepiece, regex, py-cpuinfo, pandas, opencv-python-headless, mlperf-compliance, kaggle, google-cloud-vision, google-cloud-videointelligence, google-cloud-spanner, google-cloud-pubsub, google-cloud-language, google-cloud-dlp, google-cloud-datastore, google-cloud-bigtable, google-apitools, google-api-python-client, dataclasses, apache-beam, tf-slim, tf-models-official, tensorflow-probability, scikit-learn, nltk, frozendict, tapas-table-parsing
Attempting uninstall: typing-extensions
Found existing installation: typing-extensions 3.10.0.2
Uninstalling typing-extensions-3.10.0.2:
Successfully uninstalled typing-extensions-3.10.0.2
Attempting uninstall: cachetools
Found existing installation: cachetools 4.2.4
Uninstalling cachetools-4.2.4:
Successfully uninstalled cachetools-4.2.4
Attempting uninstall: numpy
Found existing installation: numpy 1.21.5
Uninstalling numpy-1.21.5:
Successfully uninstalled numpy-1.21.5
Attempting uninstall: httplib2
Found existing installation: httplib2 0.17.4
Uninstalling httplib2-0.17.4:
Successfully uninstalled httplib2-0.17.4
Attempting uninstall: tensorflow-estimator
Found existing installation: tensorflow-estimator 2.8.0
Uninstalling tensorflow-estimator-2.8.0:
Successfully uninstalled tensorflow-estimator-2.8.0
Attempting uninstall: tensorboard
Found existing installation: tensorboard 2.8.0
Uninstalling tensorboard-2.8.0:
Successfully uninstalled tensorboard-2.8.0
Attempting uninstall: pymongo
Found existing installation: pymongo 4.0.2
Uninstalling pymongo-4.0.2:
Successfully uninstalled pymongo-4.0.2
Attempting uninstall: pyarrow
Found existing installation: pyarrow 6.0.1
Uninstalling pyarrow-6.0.1:
Successfully uninstalled pyarrow-6.0.1
Attempting uninstall: oauth2client
Found existing installation: oauth2client 4.1.3
Uninstalling oauth2client-4.1.3:
Successfully uninstalled oauth2client-4.1.3
Attempting uninstall: h5py
Found existing installation: h5py 3.1.0
Uninstalling h5py-3.1.0:
Successfully uninstalled h5py-3.1.0
Attempting uninstall: gast
Found existing installation: gast 0.5.3
Uninstalling gast-0.5.3:
Successfully uninstalled gast-0.5.3
Attempting uninstall: dill
Found existing installation: dill 0.3.4
Uninstalling dill-0.3.4:
Successfully uninstalled dill-0.3.4
Attempting uninstall: tensorflow
Found existing installation: tensorflow 2.8.0
Uninstalling tensorflow-2.8.0:
Successfully uninstalled tensorflow-2.8.0
Attempting uninstall: regex
Found existing installation: regex 2019.12.20
Uninstalling regex-2019.12.20:
Successfully uninstalled regex-2019.12.20
Attempting uninstall: pandas
Found existing installation: pandas 1.3.5
Uninstalling pandas-1.3.5:
Successfully uninstalled pandas-1.3.5
Attempting uninstall: kaggle
Found existing installation: kaggle 1.5.12
Uninstalling kaggle-1.5.12:
Successfully uninstalled kaggle-1.5.12
Attempting uninstall: google-cloud-language
Found existing installation: google-cloud-language 1.2.0
Uninstalling google-cloud-language-1.2.0:
Successfully uninstalled google-cloud-language-1.2.0
Attempting uninstall: google-cloud-datastore
Found existing installation: google-cloud-datastore 1.8.0
Uninstalling google-cloud-datastore-1.8.0:
Successfully uninstalled google-cloud-datastore-1.8.0
Attempting uninstall: google-api-python-client
Found existing installation: google-api-python-client 1.12.11
Uninstalling google-api-python-client-1.12.11:
Successfully uninstalled google-api-python-client-1.12.11
Attempting uninstall: tensorflow-probability
Found existing installation: tensorflow-probability 0.16.0
Uninstalling tensorflow-probability-0.16.0:
Successfully uninstalled tensorflow-probability-0.16.0
Attempting uninstall: scikit-learn
Found existing installation: scikit-learn 1.0.2
Uninstalling scikit-learn-1.0.2:
Successfully uninstalled scikit-learn-1.0.2
Attempting uninstall: nltk
Found existing installation: nltk 3.2.5
Uninstalling nltk-3.2.5:
Successfully uninstalled nltk-3.2.5
[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.22.2.post1 which is incompatible.
tables 3.7.0 requires numpy>=1.19.0, but you have numpy 1.18.5 which is incompatible.
pymc3 3.11.4 requires cachetools>=4.2.1, but you have cachetools 3.1.1 which is incompatible.
pydrive 1.3.1 requires oauth2client>=4.0.0, but you have oauth2client 3.0.0 which is incompatible.
multiprocess 0.70.12.2 requires dill>=0.3.4, but you have dill 0.3.1.1 which is incompatible.
jaxlib 0.3.2+cuda11.cudnn805 requires numpy>=1.19, but you have numpy 1.18.5 which is incompatible.
jax 0.3.4 requires numpy>=1.19, but you have numpy 1.18.5 which is incompatible.
imbalanced-learn 0.8.1 requires scikit-learn>=0.24, but you have scikit-learn 0.22.2.post1 which is incompatible.
google-colab 1.0.0 requires pandas>=1.1.0; python_version >= "3.0", but you have pandas 1.0.5 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.[0m
Successfully installed apache-beam-2.20.0 avro-python3-1.9.2.1 cachetools-3.1.1 dataclasses-0.6 dill-0.3.1.1 fastavro-0.21.24 fasteners-0.17.3 frozendict-1.2 gast-0.3.3 google-api-python-client-1.12.2 google-apitools-0.5.28 google-cloud-bigtable-1.0.0 google-cloud-datastore-1.7.4 google-cloud-dlp-0.13.0 google-cloud-language-1.3.0 google-cloud-pubsub-1.0.2 google-cloud-spanner-1.13.0 google-cloud-videointelligence-1.13.0 google-cloud-vision-0.42.0 grpc-google-iam-v1-0.12.3 grpcio-gcp-0.2.2 h5py-2.10.0 hdfs-2.7.0 httplib2-0.12.0 kaggle-1.5.6 mlperf-compliance-0.0.10 mock-2.0.0 nltk-3.7 numpy-1.18.5 oauth2client-3.0.0 opencv-python-headless-4.5.5.64 pandas-1.0.5 pbr-5.8.1 py-cpuinfo-8.0.0 pyarrow-0.16.0 pymongo-3.12.3 regex-2022.3.15 scikit-learn-0.22.2.post1 sentencepiece-0.1.96 tapas-table-parsing-0.0.1.dev0 tensorboard-2.2.2 tensorflow-2.2.3 tensorflow-addons-0.16.1 tensorflow-estimator-2.2.0 tensorflow-model-optimization-0.7.2 tensorflow-probability-0.10.1 tf-models-official-2.2.2 tf-slim-1.1.0 typing-3.7.4.1 typing-extensions-3.7.4.3
|
Apache-2.0
|
notebooks/sqa_predictions.ipynb
|
aniket371/tapas
|
Fetch models fom Google Storage Next we can get pretrained checkpoint from Google Storage. For the sake of speed, this is base sized model trained on [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253). Note that best results in the paper were obtained with a large model, with 24 layers instead of 12.
|
! gsutil cp gs://tapas_models/2020_04_21/tapas_sqa_base.zip . && unzip tapas_sqa_base.zip
|
Copying gs://tapas_models/2020_04_21/tapas_sqa_base.zip...
| [1 files][ 1.0 GiB/ 1.0 GiB] 51.4 MiB/s
Operation completed over 1 objects/1.0 GiB.
Archive: tapas_sqa_base.zip
replace tapas_sqa_base/model.ckpt.data-00000-of-00001? [y]es, [n]o, [A]ll, [N]one, [r]ename: y
inflating: tapas_sqa_base/model.ckpt.data-00000-of-00001 y
y
inflating: tapas_sqa_base/model.ckpt.index
inflating: tapas_sqa_base/README.txt
inflating: tapas_sqa_base/vocab.txt
inflating: tapas_sqa_base/bert_config.json
inflating: tapas_sqa_base/model.ckpt.meta
|
Apache-2.0
|
notebooks/sqa_predictions.ipynb
|
aniket371/tapas
|
Imports
|
import tensorflow.compat.v1 as tf
import os
import shutil
import csv
import pandas as pd
import IPython
tf.get_logger().setLevel('ERROR')
from tapas.utils import tf_example_utils
from tapas.protos import interaction_pb2
from tapas.utils import number_annotation_utils
from tapas.scripts import prediction_utils
|
_____no_output_____
|
Apache-2.0
|
notebooks/sqa_predictions.ipynb
|
aniket371/tapas
|
Load checkpoint for prediction Here's the prediction code, which will create and `interaction_pb2.Interaction` protobuf object, which is the datastructure we use to store examples, and then call the prediction script.
|
os.makedirs('results/sqa/tf_examples', exist_ok=True)
os.makedirs('results/sqa/model', exist_ok=True)
with open('results/sqa/model/checkpoint', 'w') as f:
f.write('model_checkpoint_path: "model.ckpt-0"')
for suffix in ['.data-00000-of-00001', '.index', '.meta']:
shutil.copyfile(f'tapas_sqa_base/model.ckpt{suffix}', f'results/sqa/model/model.ckpt-0{suffix}')
max_seq_length = 512
vocab_file = "tapas_sqa_base/vocab.txt"
config = tf_example_utils.ClassifierConversionConfig(
vocab_file=vocab_file,
max_seq_length=max_seq_length,
max_column_id=max_seq_length,
max_row_id=max_seq_length,
strip_column_names=False,
add_aggregation_candidates=False,
)
converter = tf_example_utils.ToClassifierTensorflowExample(config)
def convert_interactions_to_examples(tables_and_queries):
"""Calls Tapas converter to convert interaction to example."""
for idx, (table, queries) in enumerate(tables_and_queries):
interaction = interaction_pb2.Interaction()
for position, query in enumerate(queries):
question = interaction.questions.add()
question.original_text = query
question.id = f"{idx}-0_{position}"
for header in table[0]:
interaction.table.columns.add().text = header
for line in table[1:]:
row = interaction.table.rows.add()
for cell in line:
row.cells.add().text = cell
number_annotation_utils.add_numeric_values(interaction)
for i in range(len(interaction.questions)):
try:
yield converter.convert(interaction, i)
except ValueError as e:
print(f"Can't convert interaction: {interaction.id} error: {e}")
def write_tf_example(filename, examples):
with tf.io.TFRecordWriter(filename) as writer:
for example in examples:
writer.write(example.SerializeToString())
def predict(table_data, queries):
table = [list(map(lambda s: s.strip(), row.split("|")))
for row in table_data.split("\n") if row.strip()]
examples = convert_interactions_to_examples([(table, queries)])
write_tf_example("results/sqa/tf_examples/test.tfrecord", examples)
write_tf_example("results/sqa/tf_examples/random-split-1-dev.tfrecord", [])
! python -m tapas.run_task_main \
--task="SQA" \
--output_dir="results" \
--noloop_predict \
--test_batch_size={len(queries)} \
--tapas_verbosity="ERROR" \
--compression_type= \
--init_checkpoint="tapas_sqa_base/model.ckpt" \
--bert_config_file="tapas_sqa_base/bert_config.json" \
--mode="predict" 2> error
results_path = "results/sqa/model/test_sequence.tsv"
all_coordinates = []
df = pd.DataFrame(table[1:], columns=table[0])
display(IPython.display.HTML(df.to_html(index=False)))
print()
with open(results_path) as csvfile:
reader = csv.DictReader(csvfile, delimiter='\t')
for row in reader:
coordinates = prediction_utils.parse_coordinates(row["answer_coordinates"])
all_coordinates.append(coordinates)
answers = ', '.join([table[row + 1][col] for row, col in coordinates])
position = int(row['position'])
print(">", queries[position])
print(answers)
return all_coordinates
|
_____no_output_____
|
Apache-2.0
|
notebooks/sqa_predictions.ipynb
|
aniket371/tapas
|
Predict
|
# Example nu-1000-0
result = predict("""
Doctor_ID|Doctor_Name|Department|opd_day|Morning_time|Evening_time
1|ABCD|Nephrology|Monday|9|5
2|ABC|Opthomology|Tuesday|9|6
3|DEF|Nephrology|Wednesday|9|6
4|GHI|Gynaecology|Thursday|9|6
5|JKL|Orthopeadics|Friday|9|6
6|MNO|Cardiology|Saturday|9|6
7|PQR|Dentistry|Sunday|9|5
8|STU|Epidemology|Monday|9|6
9|WVX|ENT|Tuesday|9|5
10|GILOY|Genetics|Wednesday|9|6
11|Rajeev|Neurology|Wednesday|10|4:30
12|Makan|Immunology|Tuesday|9|4:30
13|Arora|Paediatrics|Sunday|11|4:30
14|Piyush|Radiology|Monday|11:20|2
15|Roha|Gynaecology|Wednesday|9:20|2
16|Bohra|Dentistry|Thursday|11|2
17|Rajeev Khan|Virology|Tuesday|10|2
18|Arnab|Pharmocology|Sunday|10|2
19|Muskan|ENT|Friday|10|2
20|pamela|Epidemology|Monday|10|2
21|Rohit|Radiology|Tuesday|10|2
22|Aniket|Cardiology|Saturday|10|2
23|Darbar|Genetics|Saturday|10|2
24|Suyash|Neurology|Friday|10|2
25|Abhishek|Immunology|Wednesday|10|2
26|Yogesh|Immunology|Saturday|10|2
27|Kunal|Paediatrics|Monday|10|2
28|Vimal|Pharmocology|Friday|10|2
29|Kalyan|Virology|Tuesday|10|2
30|DSS|Nephrology|Thursday|10|2
""", ["How many doctors are there in Immunology department?", "of these, which doctor is available on Saturday?"])
|
_____no_output_____
|
Apache-2.0
|
notebooks/sqa_predictions.ipynb
|
aniket371/tapas
|
Tutorial Part 6: Going Deeper On Molecular FeaturizationsOne of the most important steps of doing machine learning on molecular data is transforming this data into a form amenable to the application of learning algorithms. This process is broadly called "featurization" and involves tutrning a molecule into a vector or tensor of some sort. There are a number of different ways of doing such transformations, and the choice of featurization is often dependent on the problem at hand.In this tutorial, we explore the different featurization methods available for molecules. These featurization methods include:1. `ConvMolFeaturizer`, 2. `WeaveFeaturizer`, 3. `CircularFingerprints`4. `RDKitDescriptors`5. `BPSymmetryFunction`6. `CoulombMatrix`7. `CoulombMatrixEig`8. `AdjacencyFingerprints` ColabThis tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.[](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb) SetupTo run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment.
|
!wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh
!chmod +x Anaconda3-2019.10-Linux-x86_64.sh
!bash ./Anaconda3-2019.10-Linux-x86_64.sh -b -f -p /usr/local
!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0
import sys
sys.path.append('/usr/local/lib/python3.7/site-packages/')
|
--2020-03-07 01:06:34-- https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh
Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.130.3, 104.16.131.3, 2606:4700::6810:8303, ...
Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.130.3|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 530308481 (506M) [application/x-sh]
Saving to: ‘Anaconda3-2019.10-Linux-x86_64.sh’
Anaconda3-2019.10-L 100%[===================>] 505.74M 105MB/s in 5.1s
2020-03-07 01:06:39 (99.5 MB/s) - ‘Anaconda3-2019.10-Linux-x86_64.sh’ saved [530308481/530308481]
PREFIX=/usr/local
Unpacking payload ...
Collecting package metadata (current_repodata.json): - \ | / - \ | done
Solving environment: - \ | / - \ | / - \ | / - \ | / - \ | done
## Package Plan ##
environment location: /usr/local
added / updated specs:
- _ipyw_jlab_nb_ext_conf==0.1.0=py37_0
- _libgcc_mutex==0.1=main
- alabaster==0.7.12=py37_0
- anaconda-client==1.7.2=py37_0
- anaconda-navigator==1.9.7=py37_0
- anaconda-project==0.8.3=py_0
- anaconda==2019.10=py37_0
- asn1crypto==1.0.1=py37_0
- astroid==2.3.1=py37_0
- astropy==3.2.2=py37h7b6447c_0
- atomicwrites==1.3.0=py37_1
- attrs==19.2.0=py_0
- babel==2.7.0=py_0
- backcall==0.1.0=py37_0
- backports.functools_lru_cache==1.5=py_2
- backports.os==0.1.1=py37_0
- backports.shutil_get_terminal_size==1.0.0=py37_2
- backports.tempfile==1.0=py_1
- backports.weakref==1.0.post1=py_1
- backports==1.0=py_2
- beautifulsoup4==4.8.0=py37_0
- bitarray==1.0.1=py37h7b6447c_0
- bkcharts==0.2=py37_0
- blas==1.0=mkl
- bleach==3.1.0=py37_0
- blosc==1.16.3=hd408876_0
- bokeh==1.3.4=py37_0
- boto==2.49.0=py37_0
- bottleneck==1.2.1=py37h035aef0_1
- bzip2==1.0.8=h7b6447c_0
- ca-certificates==2019.8.28=0
- cairo==1.14.12=h8948797_3
- certifi==2019.9.11=py37_0
- cffi==1.12.3=py37h2e261b9_0
- chardet==3.0.4=py37_1003
- click==7.0=py37_0
- cloudpickle==1.2.2=py_0
- clyent==1.2.2=py37_1
- colorama==0.4.1=py37_0
- conda-build==3.18.9=py37_3
- conda-env==2.6.0=1
- conda-package-handling==1.6.0=py37h7b6447c_0
- conda-verify==3.4.2=py_1
- conda==4.7.12=py37_0
- contextlib2==0.6.0=py_0
- cryptography==2.7=py37h1ba5d50_0
- curl==7.65.3=hbc83047_0
- cycler==0.10.0=py37_0
- cython==0.29.13=py37he6710b0_0
- cytoolz==0.10.0=py37h7b6447c_0
- dask-core==2.5.2=py_0
- dask==2.5.2=py_0
- dbus==1.13.6=h746ee38_0
- decorator==4.4.0=py37_1
- defusedxml==0.6.0=py_0
- distributed==2.5.2=py_0
- docutils==0.15.2=py37_0
- entrypoints==0.3=py37_0
- et_xmlfile==1.0.1=py37_0
- expat==2.2.6=he6710b0_0
- fastcache==1.1.0=py37h7b6447c_0
- filelock==3.0.12=py_0
- flask==1.1.1=py_0
- fontconfig==2.13.0=h9420a91_0
- freetype==2.9.1=h8a8886c_1
- fribidi==1.0.5=h7b6447c_0
- fsspec==0.5.2=py_0
- future==0.17.1=py37_0
- get_terminal_size==1.0.0=haa9412d_0
- gevent==1.4.0=py37h7b6447c_0
- glib==2.56.2=hd408876_0
- glob2==0.7=py_0
- gmp==6.1.2=h6c8ec71_1
- gmpy2==2.0.8=py37h10f8cd9_2
- graphite2==1.3.13=h23475e2_0
- greenlet==0.4.15=py37h7b6447c_0
- gst-plugins-base==1.14.0=hbbd80ab_1
- gstreamer==1.14.0=hb453b48_1
- h5py==2.9.0=py37h7918eee_0
- harfbuzz==1.8.8=hffaf4a1_0
- hdf5==1.10.4=hb1b8bf9_0
- heapdict==1.0.1=py_0
- html5lib==1.0.1=py37_0
- icu==58.2=h9c2bf20_1
- idna==2.8=py37_0
- imageio==2.6.0=py37_0
- imagesize==1.1.0=py37_0
- importlib_metadata==0.23=py37_0
- intel-openmp==2019.4=243
- ipykernel==5.1.2=py37h39e3cac_0
- ipython==7.8.0=py37h39e3cac_0
- ipython_genutils==0.2.0=py37_0
- ipywidgets==7.5.1=py_0
- isort==4.3.21=py37_0
- itsdangerous==1.1.0=py37_0
- jbig==2.1=hdba287a_0
- jdcal==1.4.1=py_0
- jedi==0.15.1=py37_0
- jeepney==0.4.1=py_0
- jinja2==2.10.3=py_0
- joblib==0.13.2=py37_0
- jpeg==9b=h024ee3a_2
- json5==0.8.5=py_0
- jsonschema==3.0.2=py37_0
- jupyter==1.0.0=py37_7
- jupyter_client==5.3.3=py37_1
- jupyter_console==6.0.0=py37_0
- jupyter_core==4.5.0=py_0
- jupyterlab==1.1.4=pyhf63ae98_0
- jupyterlab_server==1.0.6=py_0
- keyring==18.0.0=py37_0
- kiwisolver==1.1.0=py37he6710b0_0
- krb5==1.16.1=h173b8e3_7
- lazy-object-proxy==1.4.2=py37h7b6447c_0
- libarchive==3.3.3=h5d8350f_5
- libcurl==7.65.3=h20c2e04_0
- libedit==3.1.20181209=hc058e9b_0
- libffi==3.2.1=hd88cf55_4
- libgcc-ng==9.1.0=hdf63c60_0
- libgfortran-ng==7.3.0=hdf63c60_0
- liblief==0.9.0=h7725739_2
- libpng==1.6.37=hbc83047_0
- libsodium==1.0.16=h1bed415_0
- libssh2==1.8.2=h1ba5d50_0
- libstdcxx-ng==9.1.0=hdf63c60_0
- libtiff==4.0.10=h2733197_2
- libtool==2.4.6=h7b6447c_5
- libuuid==1.0.3=h1bed415_2
- libxcb==1.13=h1bed415_1
- libxml2==2.9.9=hea5a465_1
- libxslt==1.1.33=h7d1a2b0_0
- llvmlite==0.29.0=py37hd408876_0
- locket==0.2.0=py37_1
- lxml==4.4.1=py37hefd8a0e_0
- lz4-c==1.8.1.2=h14c3975_0
- lzo==2.10=h49e0be7_2
- markupsafe==1.1.1=py37h7b6447c_0
- matplotlib==3.1.1=py37h5429711_0
- mccabe==0.6.1=py37_1
- mistune==0.8.4=py37h7b6447c_0
- mkl-service==2.3.0=py37he904b0f_0
- mkl==2019.4=243
- mkl_fft==1.0.14=py37ha843d7b_0
- mkl_random==1.1.0=py37hd6b4f25_0
- mock==3.0.5=py37_0
- more-itertools==7.2.0=py37_0
- mpc==1.1.0=h10f8cd9_1
- mpfr==4.0.1=hdf1c602_3
- mpmath==1.1.0=py37_0
- msgpack-python==0.6.1=py37hfd86e86_1
- multipledispatch==0.6.0=py37_0
- navigator-updater==0.2.1=py37_0
- nbconvert==5.6.0=py37_1
- nbformat==4.4.0=py37_0
- ncurses==6.1=he6710b0_1
- networkx==2.3=py_0
- nltk==3.4.5=py37_0
- nose==1.3.7=py37_2
- notebook==6.0.1=py37_0
- numba==0.45.1=py37h962f231_0
- numexpr==2.7.0=py37h9e4a6bb_0
- numpy-base==1.17.2=py37hde5b4d6_0
- numpy==1.17.2=py37haad9e8e_0
- numpydoc==0.9.1=py_0
- olefile==0.46=py37_0
- openpyxl==3.0.0=py_0
- openssl==1.1.1d=h7b6447c_2
- packaging==19.2=py_0
- pandas==0.25.1=py37he6710b0_0
- pandoc==2.2.3.2=0
- pandocfilters==1.4.2=py37_1
- pango==1.42.4=h049681c_0
- parso==0.5.1=py_0
- partd==1.0.0=py_0
- patchelf==0.9=he6710b0_3
- path.py==12.0.1=py_0
- pathlib2==2.3.5=py37_0
- patsy==0.5.1=py37_0
- pcre==8.43=he6710b0_0
- pep8==1.7.1=py37_0
- pexpect==4.7.0=py37_0
- pickleshare==0.7.5=py37_0
- pillow==6.2.0=py37h34e0f95_0
- pip==19.2.3=py37_0
- pixman==0.38.0=h7b6447c_0
- pkginfo==1.5.0.1=py37_0
- pluggy==0.13.0=py37_0
- ply==3.11=py37_0
- prometheus_client==0.7.1=py_0
- prompt_toolkit==2.0.10=py_0
- psutil==5.6.3=py37h7b6447c_0
- ptyprocess==0.6.0=py37_0
- py-lief==0.9.0=py37h7725739_2
- py==1.8.0=py37_0
- pycodestyle==2.5.0=py37_0
- pycosat==0.6.3=py37h14c3975_0
- pycparser==2.19=py37_0
- pycrypto==2.6.1=py37h14c3975_9
- pycurl==7.43.0.3=py37h1ba5d50_0
- pyflakes==2.1.1=py37_0
- pygments==2.4.2=py_0
- pylint==2.4.2=py37_0
- pyodbc==4.0.27=py37he6710b0_0
- pyopenssl==19.0.0=py37_0
- pyparsing==2.4.2=py_0
- pyqt==5.9.2=py37h05f1152_2
- pyrsistent==0.15.4=py37h7b6447c_0
- pysocks==1.7.1=py37_0
- pytables==3.5.2=py37h71ec239_1
- pytest-arraydiff==0.3=py37h39e3cac_0
- pytest-astropy==0.5.0=py37_0
- pytest-doctestplus==0.4.0=py_0
- pytest-openfiles==0.4.0=py_0
- pytest-remotedata==0.3.2=py37_0
- pytest==5.2.1=py37_0
- python-dateutil==2.8.0=py37_0
- python-libarchive-c==2.8=py37_13
- python==3.7.4=h265db76_1
- pytz==2019.3=py_0
- pywavelets==1.0.3=py37hdd07704_1
- pyyaml==5.1.2=py37h7b6447c_0
- pyzmq==18.1.0=py37he6710b0_0
- qt==5.9.7=h5867ecd_1
- qtawesome==0.6.0=py_0
- qtconsole==4.5.5=py_0
- qtpy==1.9.0=py_0
- readline==7.0=h7b6447c_5
- requests==2.22.0=py37_0
- ripgrep==0.10.0=hc07d326_0
- rope==0.14.0=py_0
- ruamel_yaml==0.15.46=py37h14c3975_0
- scikit-image==0.15.0=py37he6710b0_0
- scikit-learn==0.21.3=py37hd81dba3_0
- scipy==1.3.1=py37h7c811a0_0
- seaborn==0.9.0=py37_0
- secretstorage==3.1.1=py37_0
- send2trash==1.5.0=py37_0
- setuptools==41.4.0=py37_0
- simplegeneric==0.8.1=py37_2
- singledispatch==3.4.0.3=py37_0
- sip==4.19.8=py37hf484d3e_0
- six==1.12.0=py37_0
- snappy==1.1.7=hbae5bb6_3
- snowballstemmer==2.0.0=py_0
- sortedcollections==1.1.2=py37_0
- sortedcontainers==2.1.0=py37_0
- soupsieve==1.9.3=py37_0
- sphinx==2.2.0=py_0
- sphinxcontrib-applehelp==1.0.1=py_0
- sphinxcontrib-devhelp==1.0.1=py_0
- sphinxcontrib-htmlhelp==1.0.2=py_0
- sphinxcontrib-jsmath==1.0.1=py_0
- sphinxcontrib-qthelp==1.0.2=py_0
- sphinxcontrib-serializinghtml==1.1.3=py_0
- sphinxcontrib-websupport==1.1.2=py_0
- sphinxcontrib==1.0=py37_1
- spyder-kernels==0.5.2=py37_0
- spyder==3.3.6=py37_0
- sqlalchemy==1.3.9=py37h7b6447c_0
- sqlite==3.30.0=h7b6447c_0
- statsmodels==0.10.1=py37hdd07704_0
- sympy==1.4=py37_0
- tbb==2019.4=hfd86e86_0
- tblib==1.4.0=py_0
- terminado==0.8.2=py37_0
- testpath==0.4.2=py37_0
- tk==8.6.8=hbc83047_0
- toolz==0.10.0=py_0
- tornado==6.0.3=py37h7b6447c_0
- tqdm==4.36.1=py_0
- traitlets==4.3.3=py37_0
- unicodecsv==0.14.1=py37_0
- unixodbc==2.3.7=h14c3975_0
- urllib3==1.24.2=py37_0
- wcwidth==0.1.7=py37_0
- webencodings==0.5.1=py37_1
- werkzeug==0.16.0=py_0
- wheel==0.33.6=py37_0
- widgetsnbextension==3.5.1=py37_0
- wrapt==1.11.2=py37h7b6447c_0
- wurlitzer==1.0.3=py37_0
- xlrd==1.2.0=py37_0
- xlsxwriter==1.2.1=py_0
- xlwt==1.3.0=py37_0
- xz==5.2.4=h14c3975_4
- yaml==0.1.7=had09818_2
- zeromq==4.3.1=he6710b0_3
- zict==1.0.0=py_0
- zipp==0.6.0=py_0
- zlib==1.2.11=h7b6447c_3
- zstd==1.3.7=h0b5b093_0
The following NEW packages will be INSTALLED:
_ipyw_jlab_nb_ext~ pkgs/main/linux-64::_ipyw_jlab_nb_ext_conf-0.1.0-py37_0
_libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main
alabaster pkgs/main/linux-64::alabaster-0.7.12-py37_0
anaconda pkgs/main/linux-64::anaconda-2019.10-py37_0
anaconda-client pkgs/main/linux-64::anaconda-client-1.7.2-py37_0
anaconda-navigator pkgs/main/linux-64::anaconda-navigator-1.9.7-py37_0
anaconda-project pkgs/main/noarch::anaconda-project-0.8.3-py_0
asn1crypto pkgs/main/linux-64::asn1crypto-1.0.1-py37_0
astroid pkgs/main/linux-64::astroid-2.3.1-py37_0
astropy pkgs/main/linux-64::astropy-3.2.2-py37h7b6447c_0
atomicwrites pkgs/main/linux-64::atomicwrites-1.3.0-py37_1
attrs pkgs/main/noarch::attrs-19.2.0-py_0
babel pkgs/main/noarch::babel-2.7.0-py_0
backcall pkgs/main/linux-64::backcall-0.1.0-py37_0
backports pkgs/main/noarch::backports-1.0-py_2
backports.functoo~ pkgs/main/noarch::backports.functools_lru_cache-1.5-py_2
backports.os pkgs/main/linux-64::backports.os-0.1.1-py37_0
backports.shutil_~ pkgs/main/linux-64::backports.shutil_get_terminal_size-1.0.0-py37_2
backports.tempfile pkgs/main/noarch::backports.tempfile-1.0-py_1
backports.weakref pkgs/main/noarch::backports.weakref-1.0.post1-py_1
beautifulsoup4 pkgs/main/linux-64::beautifulsoup4-4.8.0-py37_0
bitarray pkgs/main/linux-64::bitarray-1.0.1-py37h7b6447c_0
bkcharts pkgs/main/linux-64::bkcharts-0.2-py37_0
blas pkgs/main/linux-64::blas-1.0-mkl
bleach pkgs/main/linux-64::bleach-3.1.0-py37_0
blosc pkgs/main/linux-64::blosc-1.16.3-hd408876_0
bokeh pkgs/main/linux-64::bokeh-1.3.4-py37_0
boto pkgs/main/linux-64::boto-2.49.0-py37_0
bottleneck pkgs/main/linux-64::bottleneck-1.2.1-py37h035aef0_1
bzip2 pkgs/main/linux-64::bzip2-1.0.8-h7b6447c_0
ca-certificates pkgs/main/linux-64::ca-certificates-2019.8.28-0
cairo pkgs/main/linux-64::cairo-1.14.12-h8948797_3
certifi pkgs/main/linux-64::certifi-2019.9.11-py37_0
cffi pkgs/main/linux-64::cffi-1.12.3-py37h2e261b9_0
chardet pkgs/main/linux-64::chardet-3.0.4-py37_1003
click pkgs/main/linux-64::click-7.0-py37_0
cloudpickle pkgs/main/noarch::cloudpickle-1.2.2-py_0
clyent pkgs/main/linux-64::clyent-1.2.2-py37_1
colorama pkgs/main/linux-64::colorama-0.4.1-py37_0
conda pkgs/main/linux-64::conda-4.7.12-py37_0
conda-build pkgs/main/linux-64::conda-build-3.18.9-py37_3
conda-env pkgs/main/linux-64::conda-env-2.6.0-1
conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.6.0-py37h7b6447c_0
conda-verify pkgs/main/noarch::conda-verify-3.4.2-py_1
contextlib2 pkgs/main/noarch::contextlib2-0.6.0-py_0
cryptography pkgs/main/linux-64::cryptography-2.7-py37h1ba5d50_0
curl pkgs/main/linux-64::curl-7.65.3-hbc83047_0
cycler pkgs/main/linux-64::cycler-0.10.0-py37_0
cython pkgs/main/linux-64::cython-0.29.13-py37he6710b0_0
cytoolz pkgs/main/linux-64::cytoolz-0.10.0-py37h7b6447c_0
dask pkgs/main/noarch::dask-2.5.2-py_0
dask-core pkgs/main/noarch::dask-core-2.5.2-py_0
dbus pkgs/main/linux-64::dbus-1.13.6-h746ee38_0
decorator pkgs/main/linux-64::decorator-4.4.0-py37_1
defusedxml pkgs/main/noarch::defusedxml-0.6.0-py_0
distributed pkgs/main/noarch::distributed-2.5.2-py_0
docutils pkgs/main/linux-64::docutils-0.15.2-py37_0
entrypoints pkgs/main/linux-64::entrypoints-0.3-py37_0
et_xmlfile pkgs/main/linux-64::et_xmlfile-1.0.1-py37_0
expat pkgs/main/linux-64::expat-2.2.6-he6710b0_0
fastcache pkgs/main/linux-64::fastcache-1.1.0-py37h7b6447c_0
filelock pkgs/main/noarch::filelock-3.0.12-py_0
flask pkgs/main/noarch::flask-1.1.1-py_0
fontconfig pkgs/main/linux-64::fontconfig-2.13.0-h9420a91_0
freetype pkgs/main/linux-64::freetype-2.9.1-h8a8886c_1
fribidi pkgs/main/linux-64::fribidi-1.0.5-h7b6447c_0
fsspec pkgs/main/noarch::fsspec-0.5.2-py_0
future pkgs/main/linux-64::future-0.17.1-py37_0
get_terminal_size pkgs/main/linux-64::get_terminal_size-1.0.0-haa9412d_0
gevent pkgs/main/linux-64::gevent-1.4.0-py37h7b6447c_0
glib pkgs/main/linux-64::glib-2.56.2-hd408876_0
glob2 pkgs/main/noarch::glob2-0.7-py_0
gmp pkgs/main/linux-64::gmp-6.1.2-h6c8ec71_1
gmpy2 pkgs/main/linux-64::gmpy2-2.0.8-py37h10f8cd9_2
graphite2 pkgs/main/linux-64::graphite2-1.3.13-h23475e2_0
greenlet pkgs/main/linux-64::greenlet-0.4.15-py37h7b6447c_0
gst-plugins-base pkgs/main/linux-64::gst-plugins-base-1.14.0-hbbd80ab_1
gstreamer pkgs/main/linux-64::gstreamer-1.14.0-hb453b48_1
h5py pkgs/main/linux-64::h5py-2.9.0-py37h7918eee_0
harfbuzz pkgs/main/linux-64::harfbuzz-1.8.8-hffaf4a1_0
hdf5 pkgs/main/linux-64::hdf5-1.10.4-hb1b8bf9_0
heapdict pkgs/main/noarch::heapdict-1.0.1-py_0
html5lib pkgs/main/linux-64::html5lib-1.0.1-py37_0
icu pkgs/main/linux-64::icu-58.2-h9c2bf20_1
idna pkgs/main/linux-64::idna-2.8-py37_0
imageio pkgs/main/linux-64::imageio-2.6.0-py37_0
imagesize pkgs/main/linux-64::imagesize-1.1.0-py37_0
importlib_metadata pkgs/main/linux-64::importlib_metadata-0.23-py37_0
intel-openmp pkgs/main/linux-64::intel-openmp-2019.4-243
ipykernel pkgs/main/linux-64::ipykernel-5.1.2-py37h39e3cac_0
ipython pkgs/main/linux-64::ipython-7.8.0-py37h39e3cac_0
ipython_genutils pkgs/main/linux-64::ipython_genutils-0.2.0-py37_0
ipywidgets pkgs/main/noarch::ipywidgets-7.5.1-py_0
isort pkgs/main/linux-64::isort-4.3.21-py37_0
itsdangerous pkgs/main/linux-64::itsdangerous-1.1.0-py37_0
jbig pkgs/main/linux-64::jbig-2.1-hdba287a_0
jdcal pkgs/main/noarch::jdcal-1.4.1-py_0
jedi pkgs/main/linux-64::jedi-0.15.1-py37_0
jeepney pkgs/main/noarch::jeepney-0.4.1-py_0
jinja2 pkgs/main/noarch::jinja2-2.10.3-py_0
joblib pkgs/main/linux-64::joblib-0.13.2-py37_0
jpeg pkgs/main/linux-64::jpeg-9b-h024ee3a_2
json5 pkgs/main/noarch::json5-0.8.5-py_0
jsonschema pkgs/main/linux-64::jsonschema-3.0.2-py37_0
jupyter pkgs/main/linux-64::jupyter-1.0.0-py37_7
jupyter_client pkgs/main/linux-64::jupyter_client-5.3.3-py37_1
jupyter_console pkgs/main/linux-64::jupyter_console-6.0.0-py37_0
jupyter_core pkgs/main/noarch::jupyter_core-4.5.0-py_0
jupyterlab pkgs/main/noarch::jupyterlab-1.1.4-pyhf63ae98_0
jupyterlab_server pkgs/main/noarch::jupyterlab_server-1.0.6-py_0
keyring pkgs/main/linux-64::keyring-18.0.0-py37_0
kiwisolver pkgs/main/linux-64::kiwisolver-1.1.0-py37he6710b0_0
krb5 pkgs/main/linux-64::krb5-1.16.1-h173b8e3_7
lazy-object-proxy pkgs/main/linux-64::lazy-object-proxy-1.4.2-py37h7b6447c_0
libarchive pkgs/main/linux-64::libarchive-3.3.3-h5d8350f_5
libcurl pkgs/main/linux-64::libcurl-7.65.3-h20c2e04_0
libedit pkgs/main/linux-64::libedit-3.1.20181209-hc058e9b_0
libffi pkgs/main/linux-64::libffi-3.2.1-hd88cf55_4
libgcc-ng pkgs/main/linux-64::libgcc-ng-9.1.0-hdf63c60_0
libgfortran-ng pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0
liblief pkgs/main/linux-64::liblief-0.9.0-h7725739_2
libpng pkgs/main/linux-64::libpng-1.6.37-hbc83047_0
libsodium pkgs/main/linux-64::libsodium-1.0.16-h1bed415_0
libssh2 pkgs/main/linux-64::libssh2-1.8.2-h1ba5d50_0
libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.1.0-hdf63c60_0
libtiff pkgs/main/linux-64::libtiff-4.0.10-h2733197_2
libtool pkgs/main/linux-64::libtool-2.4.6-h7b6447c_5
libuuid pkgs/main/linux-64::libuuid-1.0.3-h1bed415_2
libxcb pkgs/main/linux-64::libxcb-1.13-h1bed415_1
libxml2 pkgs/main/linux-64::libxml2-2.9.9-hea5a465_1
libxslt pkgs/main/linux-64::libxslt-1.1.33-h7d1a2b0_0
llvmlite pkgs/main/linux-64::llvmlite-0.29.0-py37hd408876_0
locket pkgs/main/linux-64::locket-0.2.0-py37_1
lxml pkgs/main/linux-64::lxml-4.4.1-py37hefd8a0e_0
lz4-c pkgs/main/linux-64::lz4-c-1.8.1.2-h14c3975_0
lzo pkgs/main/linux-64::lzo-2.10-h49e0be7_2
markupsafe pkgs/main/linux-64::markupsafe-1.1.1-py37h7b6447c_0
matplotlib pkgs/main/linux-64::matplotlib-3.1.1-py37h5429711_0
mccabe pkgs/main/linux-64::mccabe-0.6.1-py37_1
mistune pkgs/main/linux-64::mistune-0.8.4-py37h7b6447c_0
mkl pkgs/main/linux-64::mkl-2019.4-243
mkl-service pkgs/main/linux-64::mkl-service-2.3.0-py37he904b0f_0
mkl_fft pkgs/main/linux-64::mkl_fft-1.0.14-py37ha843d7b_0
mkl_random pkgs/main/linux-64::mkl_random-1.1.0-py37hd6b4f25_0
mock pkgs/main/linux-64::mock-3.0.5-py37_0
more-itertools pkgs/main/linux-64::more-itertools-7.2.0-py37_0
mpc pkgs/main/linux-64::mpc-1.1.0-h10f8cd9_1
mpfr pkgs/main/linux-64::mpfr-4.0.1-hdf1c602_3
mpmath pkgs/main/linux-64::mpmath-1.1.0-py37_0
msgpack-python pkgs/main/linux-64::msgpack-python-0.6.1-py37hfd86e86_1
multipledispatch pkgs/main/linux-64::multipledispatch-0.6.0-py37_0
navigator-updater pkgs/main/linux-64::navigator-updater-0.2.1-py37_0
nbconvert pkgs/main/linux-64::nbconvert-5.6.0-py37_1
nbformat pkgs/main/linux-64::nbformat-4.4.0-py37_0
ncurses pkgs/main/linux-64::ncurses-6.1-he6710b0_1
networkx pkgs/main/noarch::networkx-2.3-py_0
nltk pkgs/main/linux-64::nltk-3.4.5-py37_0
nose pkgs/main/linux-64::nose-1.3.7-py37_2
notebook pkgs/main/linux-64::notebook-6.0.1-py37_0
numba pkgs/main/linux-64::numba-0.45.1-py37h962f231_0
numexpr pkgs/main/linux-64::numexpr-2.7.0-py37h9e4a6bb_0
numpy pkgs/main/linux-64::numpy-1.17.2-py37haad9e8e_0
numpy-base pkgs/main/linux-64::numpy-base-1.17.2-py37hde5b4d6_0
numpydoc pkgs/main/noarch::numpydoc-0.9.1-py_0
olefile pkgs/main/linux-64::olefile-0.46-py37_0
openpyxl pkgs/main/noarch::openpyxl-3.0.0-py_0
openssl pkgs/main/linux-64::openssl-1.1.1d-h7b6447c_2
packaging pkgs/main/noarch::packaging-19.2-py_0
pandas pkgs/main/linux-64::pandas-0.25.1-py37he6710b0_0
pandoc pkgs/main/linux-64::pandoc-2.2.3.2-0
pandocfilters pkgs/main/linux-64::pandocfilters-1.4.2-py37_1
pango pkgs/main/linux-64::pango-1.42.4-h049681c_0
parso pkgs/main/noarch::parso-0.5.1-py_0
partd pkgs/main/noarch::partd-1.0.0-py_0
patchelf pkgs/main/linux-64::patchelf-0.9-he6710b0_3
path.py pkgs/main/noarch::path.py-12.0.1-py_0
pathlib2 pkgs/main/linux-64::pathlib2-2.3.5-py37_0
patsy pkgs/main/linux-64::patsy-0.5.1-py37_0
pcre pkgs/main/linux-64::pcre-8.43-he6710b0_0
pep8 pkgs/main/linux-64::pep8-1.7.1-py37_0
pexpect pkgs/main/linux-64::pexpect-4.7.0-py37_0
pickleshare pkgs/main/linux-64::pickleshare-0.7.5-py37_0
pillow pkgs/main/linux-64::pillow-6.2.0-py37h34e0f95_0
pip pkgs/main/linux-64::pip-19.2.3-py37_0
pixman pkgs/main/linux-64::pixman-0.38.0-h7b6447c_0
pkginfo pkgs/main/linux-64::pkginfo-1.5.0.1-py37_0
pluggy pkgs/main/linux-64::pluggy-0.13.0-py37_0
ply pkgs/main/linux-64::ply-3.11-py37_0
prometheus_client pkgs/main/noarch::prometheus_client-0.7.1-py_0
prompt_toolkit pkgs/main/noarch::prompt_toolkit-2.0.10-py_0
psutil pkgs/main/linux-64::psutil-5.6.3-py37h7b6447c_0
ptyprocess pkgs/main/linux-64::ptyprocess-0.6.0-py37_0
py pkgs/main/linux-64::py-1.8.0-py37_0
py-lief pkgs/main/linux-64::py-lief-0.9.0-py37h7725739_2
pycodestyle pkgs/main/linux-64::pycodestyle-2.5.0-py37_0
pycosat pkgs/main/linux-64::pycosat-0.6.3-py37h14c3975_0
pycparser pkgs/main/linux-64::pycparser-2.19-py37_0
pycrypto pkgs/main/linux-64::pycrypto-2.6.1-py37h14c3975_9
pycurl pkgs/main/linux-64::pycurl-7.43.0.3-py37h1ba5d50_0
pyflakes pkgs/main/linux-64::pyflakes-2.1.1-py37_0
pygments pkgs/main/noarch::pygments-2.4.2-py_0
pylint pkgs/main/linux-64::pylint-2.4.2-py37_0
pyodbc pkgs/main/linux-64::pyodbc-4.0.27-py37he6710b0_0
pyopenssl pkgs/main/linux-64::pyopenssl-19.0.0-py37_0
pyparsing pkgs/main/noarch::pyparsing-2.4.2-py_0
pyqt pkgs/main/linux-64::pyqt-5.9.2-py37h05f1152_2
pyrsistent pkgs/main/linux-64::pyrsistent-0.15.4-py37h7b6447c_0
pysocks pkgs/main/linux-64::pysocks-1.7.1-py37_0
pytables pkgs/main/linux-64::pytables-3.5.2-py37h71ec239_1
pytest pkgs/main/linux-64::pytest-5.2.1-py37_0
pytest-arraydiff pkgs/main/linux-64::pytest-arraydiff-0.3-py37h39e3cac_0
pytest-astropy pkgs/main/linux-64::pytest-astropy-0.5.0-py37_0
pytest-doctestplus pkgs/main/noarch::pytest-doctestplus-0.4.0-py_0
pytest-openfiles pkgs/main/noarch::pytest-openfiles-0.4.0-py_0
pytest-remotedata pkgs/main/linux-64::pytest-remotedata-0.3.2-py37_0
python pkgs/main/linux-64::python-3.7.4-h265db76_1
python-dateutil pkgs/main/linux-64::python-dateutil-2.8.0-py37_0
python-libarchive~ pkgs/main/linux-64::python-libarchive-c-2.8-py37_13
pytz pkgs/main/noarch::pytz-2019.3-py_0
pywavelets pkgs/main/linux-64::pywavelets-1.0.3-py37hdd07704_1
pyyaml pkgs/main/linux-64::pyyaml-5.1.2-py37h7b6447c_0
pyzmq pkgs/main/linux-64::pyzmq-18.1.0-py37he6710b0_0
qt pkgs/main/linux-64::qt-5.9.7-h5867ecd_1
qtawesome pkgs/main/noarch::qtawesome-0.6.0-py_0
qtconsole pkgs/main/noarch::qtconsole-4.5.5-py_0
qtpy pkgs/main/noarch::qtpy-1.9.0-py_0
readline pkgs/main/linux-64::readline-7.0-h7b6447c_5
requests pkgs/main/linux-64::requests-2.22.0-py37_0
ripgrep pkgs/main/linux-64::ripgrep-0.10.0-hc07d326_0
rope pkgs/main/noarch::rope-0.14.0-py_0
ruamel_yaml pkgs/main/linux-64::ruamel_yaml-0.15.46-py37h14c3975_0
scikit-image pkgs/main/linux-64::scikit-image-0.15.0-py37he6710b0_0
scikit-learn pkgs/main/linux-64::scikit-learn-0.21.3-py37hd81dba3_0
scipy pkgs/main/linux-64::scipy-1.3.1-py37h7c811a0_0
seaborn pkgs/main/linux-64::seaborn-0.9.0-py37_0
secretstorage pkgs/main/linux-64::secretstorage-3.1.1-py37_0
send2trash pkgs/main/linux-64::send2trash-1.5.0-py37_0
setuptools pkgs/main/linux-64::setuptools-41.4.0-py37_0
simplegeneric pkgs/main/linux-64::simplegeneric-0.8.1-py37_2
singledispatch pkgs/main/linux-64::singledispatch-3.4.0.3-py37_0
sip pkgs/main/linux-64::sip-4.19.8-py37hf484d3e_0
six pkgs/main/linux-64::six-1.12.0-py37_0
snappy pkgs/main/linux-64::snappy-1.1.7-hbae5bb6_3
snowballstemmer pkgs/main/noarch::snowballstemmer-2.0.0-py_0
sortedcollections pkgs/main/linux-64::sortedcollections-1.1.2-py37_0
sortedcontainers pkgs/main/linux-64::sortedcontainers-2.1.0-py37_0
soupsieve pkgs/main/linux-64::soupsieve-1.9.3-py37_0
sphinx pkgs/main/noarch::sphinx-2.2.0-py_0
sphinxcontrib pkgs/main/linux-64::sphinxcontrib-1.0-py37_1
sphinxcontrib-app~ pkgs/main/noarch::sphinxcontrib-applehelp-1.0.1-py_0
sphinxcontrib-dev~ pkgs/main/noarch::sphinxcontrib-devhelp-1.0.1-py_0
sphinxcontrib-htm~ pkgs/main/noarch::sphinxcontrib-htmlhelp-1.0.2-py_0
sphinxcontrib-jsm~ pkgs/main/noarch::sphinxcontrib-jsmath-1.0.1-py_0
sphinxcontrib-qth~ pkgs/main/noarch::sphinxcontrib-qthelp-1.0.2-py_0
sphinxcontrib-ser~ pkgs/main/noarch::sphinxcontrib-serializinghtml-1.1.3-py_0
sphinxcontrib-web~ pkgs/main/noarch::sphinxcontrib-websupport-1.1.2-py_0
spyder pkgs/main/linux-64::spyder-3.3.6-py37_0
spyder-kernels pkgs/main/linux-64::spyder-kernels-0.5.2-py37_0
sqlalchemy pkgs/main/linux-64::sqlalchemy-1.3.9-py37h7b6447c_0
sqlite pkgs/main/linux-64::sqlite-3.30.0-h7b6447c_0
statsmodels pkgs/main/linux-64::statsmodels-0.10.1-py37hdd07704_0
sympy pkgs/main/linux-64::sympy-1.4-py37_0
tbb pkgs/main/linux-64::tbb-2019.4-hfd86e86_0
tblib pkgs/main/noarch::tblib-1.4.0-py_0
terminado pkgs/main/linux-64::terminado-0.8.2-py37_0
testpath pkgs/main/linux-64::testpath-0.4.2-py37_0
tk pkgs/main/linux-64::tk-8.6.8-hbc83047_0
toolz pkgs/main/noarch::toolz-0.10.0-py_0
tornado pkgs/main/linux-64::tornado-6.0.3-py37h7b6447c_0
tqdm pkgs/main/noarch::tqdm-4.36.1-py_0
traitlets pkgs/main/linux-64::traitlets-4.3.3-py37_0
unicodecsv pkgs/main/linux-64::unicodecsv-0.14.1-py37_0
unixodbc pkgs/main/linux-64::unixodbc-2.3.7-h14c3975_0
urllib3 pkgs/main/linux-64::urllib3-1.24.2-py37_0
wcwidth pkgs/main/linux-64::wcwidth-0.1.7-py37_0
webencodings pkgs/main/linux-64::webencodings-0.5.1-py37_1
werkzeug pkgs/main/noarch::werkzeug-0.16.0-py_0
wheel pkgs/main/linux-64::wheel-0.33.6-py37_0
widgetsnbextension pkgs/main/linux-64::widgetsnbextension-3.5.1-py37_0
wrapt pkgs/main/linux-64::wrapt-1.11.2-py37h7b6447c_0
wurlitzer pkgs/main/linux-64::wurlitzer-1.0.3-py37_0
xlrd pkgs/main/linux-64::xlrd-1.2.0-py37_0
xlsxwriter pkgs/main/noarch::xlsxwriter-1.2.1-py_0
xlwt pkgs/main/linux-64::xlwt-1.3.0-py37_0
xz pkgs/main/linux-64::xz-5.2.4-h14c3975_4
yaml pkgs/main/linux-64::yaml-0.1.7-had09818_2
zeromq pkgs/main/linux-64::zeromq-4.3.1-he6710b0_3
zict pkgs/main/noarch::zict-1.0.0-py_0
zipp pkgs/main/noarch::zipp-0.6.0-py_0
zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3
zstd pkgs/main/linux-64::zstd-1.3.7-h0b5b093_0
Preparing transaction: - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
Executing transaction: - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done
installation finished.
WARNING:
You currently have a PYTHONPATH environment variable set. This may cause
unexpected behavior when running the Python interpreter in Anaconda3.
For best results, please verify that your PYTHONPATH only points to
directories of packages that are compatible with the Python interpreter
in Anaconda3: /usr/local
Collecting package metadata (current_repodata.json): - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
Solving environment: | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ failed with initial frozen solve. Retrying with flexible solve.
Solving environment: / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
Solving environment: / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done
==> WARNING: A newer version of conda exists. <==
current version: 4.7.12
latest version: 4.8.2
Please update conda by running
$ conda update -n base -c defaults conda
## Package Plan ##
environment location: /usr/local
added / updated specs:
- deepchem-gpu=2.3.0
The following packages will be downloaded:
package | build
---------------------------|-----------------
_py-xgboost-mutex-2.0 | cpu_0 8 KB conda-forge
_tflow_select-2.1.0 | gpu 2 KB
absl-py-0.9.0 | py37_0 162 KB conda-forge
astor-0.7.1 | py_0 22 KB conda-forge
c-ares-1.15.0 | h516909a_1001 100 KB conda-forge
certifi-2019.9.11 | py37_0 147 KB conda-forge
conda-4.8.2 | py37_0 3.0 MB conda-forge
cudatoolkit-10.1.243 | h6bb024c_0 347.4 MB
cudnn-7.6.5 | cuda10.1_0 179.9 MB
cupti-10.1.168 | 0 1.4 MB
deepchem-gpu-2.3.0 | py37_0 2.1 MB deepchem
fftw3f-3.3.4 | 2 1.2 MB omnia
gast-0.3.3 | py_0 12 KB conda-forge
google-pasta-0.1.8 | py_0 42 KB conda-forge
grpcio-1.23.0 | py37he9ae1f9_0 1.1 MB conda-forge
keras-applications-1.0.8 | py_1 30 KB conda-forge
keras-preprocessing-1.1.0 | py_0 33 KB conda-forge
libboost-1.67.0 | h46d08c1_4 13.0 MB
libprotobuf-3.11.4 | h8b12597_0 4.8 MB conda-forge
libxgboost-0.90 | he1b5a44_4 2.4 MB conda-forge
markdown-3.2.1 | py_0 61 KB conda-forge
mdtraj-1.9.3 | py37h00575c5_0 1.9 MB conda-forge
openmm-7.4.1 |py37_cuda101_rc_1 11.9 MB omnia
pdbfixer-1.6 | py37_0 190 KB omnia
protobuf-3.11.4 | py37he1b5a44_0 699 KB conda-forge
py-boost-1.67.0 | py37h04863e7_4 278 KB
py-xgboost-0.90 | py37_4 73 KB conda-forge
rdkit-2019.09.3.0 | py37hc20afe1_1 23.7 MB rdkit
simdna-0.4.2 | py_0 627 KB deepchem
tensorboard-1.14.0 | py37_0 3.2 MB conda-forge
tensorflow-1.14.0 |gpu_py37h74c33d7_0 4 KB
tensorflow-base-1.14.0 |gpu_py37he45bfe2_0 146.3 MB
tensorflow-estimator-1.14.0| py37h5ca1d4c_0 645 KB conda-forge
tensorflow-gpu-1.14.0 | h0d30ee6_0 3 KB
termcolor-1.1.0 | py_2 6 KB conda-forge
xgboost-0.90 | py37he1b5a44_4 11 KB conda-forge
------------------------------------------------------------
Total: 746.5 MB
The following NEW packages will be INSTALLED:
_py-xgboost-mutex conda-forge/linux-64::_py-xgboost-mutex-2.0-cpu_0
_tflow_select pkgs/main/linux-64::_tflow_select-2.1.0-gpu
absl-py conda-forge/linux-64::absl-py-0.9.0-py37_0
astor conda-forge/noarch::astor-0.7.1-py_0
c-ares conda-forge/linux-64::c-ares-1.15.0-h516909a_1001
cudatoolkit pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0
cudnn pkgs/main/linux-64::cudnn-7.6.5-cuda10.1_0
cupti pkgs/main/linux-64::cupti-10.1.168-0
deepchem-gpu deepchem/linux-64::deepchem-gpu-2.3.0-py37_0
fftw3f omnia/linux-64::fftw3f-3.3.4-2
gast conda-forge/noarch::gast-0.3.3-py_0
google-pasta conda-forge/noarch::google-pasta-0.1.8-py_0
grpcio conda-forge/linux-64::grpcio-1.23.0-py37he9ae1f9_0
keras-applications conda-forge/noarch::keras-applications-1.0.8-py_1
keras-preprocessi~ conda-forge/noarch::keras-preprocessing-1.1.0-py_0
libboost pkgs/main/linux-64::libboost-1.67.0-h46d08c1_4
libprotobuf conda-forge/linux-64::libprotobuf-3.11.4-h8b12597_0
libxgboost conda-forge/linux-64::libxgboost-0.90-he1b5a44_4
markdown conda-forge/noarch::markdown-3.2.1-py_0
mdtraj conda-forge/linux-64::mdtraj-1.9.3-py37h00575c5_0
openmm omnia/linux-64::openmm-7.4.1-py37_cuda101_rc_1
pdbfixer omnia/linux-64::pdbfixer-1.6-py37_0
protobuf conda-forge/linux-64::protobuf-3.11.4-py37he1b5a44_0
py-boost pkgs/main/linux-64::py-boost-1.67.0-py37h04863e7_4
py-xgboost conda-forge/linux-64::py-xgboost-0.90-py37_4
rdkit rdkit/linux-64::rdkit-2019.09.3.0-py37hc20afe1_1
simdna deepchem/noarch::simdna-0.4.2-py_0
tensorboard conda-forge/linux-64::tensorboard-1.14.0-py37_0
tensorflow pkgs/main/linux-64::tensorflow-1.14.0-gpu_py37h74c33d7_0
tensorflow-base pkgs/main/linux-64::tensorflow-base-1.14.0-gpu_py37he45bfe2_0
tensorflow-estima~ conda-forge/linux-64::tensorflow-estimator-1.14.0-py37h5ca1d4c_0
tensorflow-gpu pkgs/main/linux-64::tensorflow-gpu-1.14.0-h0d30ee6_0
termcolor conda-forge/noarch::termcolor-1.1.0-py_2
xgboost conda-forge/linux-64::xgboost-0.90-py37he1b5a44_4
The following packages will be UPDATED:
conda pkgs/main::conda-4.7.12-py37_0 --> conda-forge::conda-4.8.2-py37_0
The following packages will be SUPERSEDED by a higher-priority channel:
certifi pkgs/main --> conda-forge
Downloading and Extracting Packages
keras-applications-1 | 30 KB | : 100% 1.0/1 [00:00<00:00, 8.82it/s]
libboost-1.67.0 | 13.0 MB | : 100% 1.0/1 [00:01<00:00, 1.85s/it]
absl-py-0.9.0 | 162 KB | : 100% 1.0/1 [00:00<00:00, 11.13it/s]
libxgboost-0.90 | 2.4 MB | : 100% 1.0/1 [00:00<00:00, 2.16it/s]
cupti-10.1.168 | 1.4 MB | : 100% 1.0/1 [00:00<00:00, 7.39it/s]
termcolor-1.1.0 | 6 KB | : 100% 1.0/1 [00:00<00:00, 22.33it/s]
tensorflow-base-1.14 | 146.3 MB | : 100% 1.0/1 [00:14<00:00, 14.12s/it]
tensorboard-1.14.0 | 3.2 MB | : 100% 1.0/1 [00:00<00:00, 1.87it/s]
cudnn-7.6.5 | 179.9 MB | : 100% 1.0/1 [00:10<00:00, 10.91s/it]
conda-4.8.2 | 3.0 MB | : 100% 1.0/1 [00:00<00:00, 1.22it/s]
py-boost-1.67.0 | 278 KB | : 100% 1.0/1 [00:00<00:00, 8.26it/s]
py-xgboost-0.90 | 73 KB | : 100% 1.0/1 [00:00<00:00, 18.94it/s]
tensorflow-gpu-1.14. | 3 KB | : 100% 1.0/1 [00:00<00:00, 9.85it/s]
mdtraj-1.9.3 | 1.9 MB | : 100% 1.0/1 [00:00<00:00, 2.17it/s]
rdkit-2019.09.3.0 | 23.7 MB | : 100% 1.0/1 [00:05<00:00, 76.64s/it]
deepchem-gpu-2.3.0 | 2.1 MB | : 100% 1.0/1 [00:00<00:00, 50.91s/it]
grpcio-1.23.0 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 4.14it/s]
_py-xgboost-mutex-2. | 8 KB | : 100% 1.0/1 [00:00<00:00, 27.43it/s]
libprotobuf-3.11.4 | 4.8 MB | : 100% 1.0/1 [00:01<00:00, 1.08s/it]
keras-preprocessing- | 33 KB | : 100% 1.0/1 [00:00<00:00, 22.50it/s]
markdown-3.2.1 | 61 KB | : 100% 1.0/1 [00:00<00:00, 20.73it/s]
google-pasta-0.1.8 | 42 KB | : 100% 1.0/1 [00:00<00:00, 11.05it/s]
protobuf-3.11.4 | 699 KB | : 100% 1.0/1 [00:00<00:00, 4.10it/s]
_tflow_select-2.1.0 | 2 KB | : 100% 1.0/1 [00:00<00:00, 10.36it/s]
simdna-0.4.2 | 627 KB | : 100% 1.0/1 [00:00<00:00, 2.80it/s]
c-ares-1.15.0 | 100 KB | : 100% 1.0/1 [00:00<00:00, 13.50it/s]
gast-0.3.3 | 12 KB | : 100% 1.0/1 [00:00<00:00, 20.80it/s]
certifi-2019.9.11 | 147 KB | : 100% 1.0/1 [00:00<00:00, 7.10it/s]
fftw3f-3.3.4 | 1.2 MB | : 100% 1.0/1 [00:00<00:00, 12.56s/it]
openmm-7.4.1 | 11.9 MB | : 100% 1.0/1 [00:03<00:00, 108.64s/it]
tensorflow-1.14.0 | 4 KB | : 100% 1.0/1 [00:00<00:00, 10.64it/s]
tensorflow-estimator | 645 KB | : 100% 1.0/1 [00:00<00:00, 4.16it/s]
astor-0.7.1 | 22 KB | : 100% 1.0/1 [00:00<00:00, 26.30it/s]
xgboost-0.90 | 11 KB | : 100% 1.0/1 [00:00<00:00, 32.86it/s]
cudatoolkit-10.1.243 | 347.4 MB | : 100% 1.0/1 [00:19<00:00, 19.76s/it]
pdbfixer-1.6 | 190 KB | : 100% 1.0/1 [00:00<00:00, 1.50it/s]
Preparing transaction: \ | / - \ | / - \ | / done
Verifying transaction: \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
Executing transaction: | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ done
|
MIT
|
examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
|
patrickphatnguyen/deepchem
|
Let's start with some basic imports
|
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import numpy as np
from rdkit import Chem
from deepchem.feat import ConvMolFeaturizer, WeaveFeaturizer, CircularFingerprint
from deepchem.feat import AdjacencyFingerprint, RDKitDescriptors
from deepchem.feat import BPSymmetryFunctionInput, CoulombMatrix, CoulombMatrixEig
from deepchem.utils import conformers
|
/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.
warnings.warn(msg, category=FutureWarning)
|
MIT
|
examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
|
patrickphatnguyen/deepchem
|
We use `propane`( $CH_3 CH_2 CH_3 $ ) as a running example throughout this tutorial. Many of the featurization methods use conformers or the molecules. A conformer can be generated using the `ConformerGenerator` class in `deepchem.utils.conformers`. RDKitDescriptors `RDKitDescriptors` featurizes a molecule by computing descriptors values for specified descriptors. Intrinsic to the featurizer is a set of allowed descriptors, which can be accessed using `RDKitDescriptors.allowedDescriptors`.The featurizer uses the descriptors in `rdkit.Chem.Descriptors.descList`, checks if they are in the list of allowed descriptors and computes the descriptor value for the molecule.
|
example_smile = "CCC"
example_mol = Chem.MolFromSmiles(example_smile)
|
_____no_output_____
|
MIT
|
examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
|
patrickphatnguyen/deepchem
|
Let's check the allowed list of descriptors. As you will see shortly, there's a wide range of chemical properties that RDKit computes for us.
|
for descriptor in RDKitDescriptors.allowedDescriptors:
print(descriptor)
rdkit_desc = RDKitDescriptors()
features = rdkit_desc._featurize(example_mol)
print('The number of descriptors present are: ', len(features))
|
The number of descriptors present are: 111
|
MIT
|
examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
|
patrickphatnguyen/deepchem
|
BPSymmetryFunction `Behler-Parinello Symmetry function` or `BPSymmetryFunction` featurizes a molecule by computing the atomic number and coordinates for each atom in the molecule. The features can be used as input for symmetry functions, like `RadialSymmetry`, `DistanceMatrix` and `DistanceCutoff` . More details on these symmetry functions can be found in [this paper](https://journals.aps.org/prl/pdf/10.1103/PhysRevLett.98.146401). These functions can be found in `deepchem.feat.coulomb_matrices`The featurizer takes in `max_atoms` as an argument. As input, it takes in a conformer of the molecule and computes:1. coordinates of every atom in the molecule (in Bohr units)2. the atomic numbers for all atoms. These features are concantenated and padded with zeros to account for different number of atoms, across molecules.
|
example_smile = "CCC"
example_mol = Chem.MolFromSmiles(example_smile)
engine = conformers.ConformerGenerator(max_conformers=1)
example_mol = engine.generate_conformers(example_mol)
|
_____no_output_____
|
MIT
|
examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
|
patrickphatnguyen/deepchem
|
Let's now take a look at the actual featurized matrix that comes out.
|
bp_sym = BPSymmetryFunctionInput(max_atoms=20)
features = bp_sym._featurize(mol=example_mol)
features
|
_____no_output_____
|
MIT
|
examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
|
patrickphatnguyen/deepchem
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.