markdown
stringlengths 0
1.02M
| code
stringlengths 0
832k
| output
stringlengths 0
1.02M
| license
stringlengths 3
36
| path
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Simulation
|
n_loop = 10
rnd = np.random.RandomState(7)
labels = np.arange(C).repeat(100)
results = {}
for N in ns:
num_iters = int(len(labels) / N)
total_samples_for_bounds = float(num_iters * N * (n_loop))
for _ in range(n_loop):
rnd.shuffle(labels)
for batch_id in range(len(labels) // N):
if len(set(labels[N * batch_id:N * (batch_id + 1)])) == C:
results[N] = results.get(N, 0.) + N / total_samples_for_bounds
else:
results[N] = results.get(N, 0.) + 0.
xs = []
ys = []
for k, v in results.items():
print(k, v)
ys.append(v)
xs.append(k)
plt.plot(ns, ps, label="Theoretical")
plt.plot(xs, ys, label="Empirical")
plt.ylabel("probability")
plt.xlabel("$K+1$")
plt.title("CIFAR-100 simulation")
plt.legend()
|
_____no_output_____
|
MIT
|
code/notebooks/coupon.ipynb
|
nzw0301/Understanding-Negative-Samples-in-Instance-Discriminative-Self-supervised-Representation-Learning
|
3K Rice Genome GWAS Dataset Export Usage Data for this was exported as single Hail MatrixTable (`.mt`) as well as individual variants (`csv.gz`), samples (`csv`), and call datasets (`zarr`).
|
from pathlib import Path
import pandas as pd
import numpy as np
import hail as hl
import zarr
hl.init()
path = Path('~/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export').expanduser()
path
!du -sh {str(path)}/*
|
582M /home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.calls.zarr
336K /home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.cols.csv
471M /home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.mt
7.5M /home/eczech/data/gwas/rice-snpseek/1M_GWAS_SNP_Dataset/rg-3k-gwas-export/rg-3k-gwas-export.rows.csv.gz
|
Apache-2.0
|
notebooks/organism/rice/rg-export-usage.ipynb
|
tomwhite/gwas-analysis
|
Hail
|
# The entire table with row, col, and call data:
hl.read_matrix_table(str(path / 'rg-3k-gwas-export.mt')).describe()
|
----------------------------------------
Global fields:
None
----------------------------------------
Column fields:
's': str
'acc_seq_no': int64
'acc_stock_id': int64
'acc_gs_acc': float64
'acc_gs_variety_name': str
'acc_igrc_acc_src': int64
'pt_APANTH_REPRO': float64
'pt_APSH': float64
'pt_APCO_REV_POST': float64
'pt_APCO_REV_REPRO': float64
'pt_AWCO_LREV': float64
'pt_AWCO_REV': float64
'pt_AWDIST': float64
'pt_BLANTHPR_VEG': float64
'pt_BLANTHDI_VEG': float64
'pt_BLPUB_VEG': float64
'pt_BLSCO_ANTH_VEG': float64
'pt_BLSCO_REV_VEG': float64
'pt_CCO_REV_VEG': float64
'pt_CUAN_REPRO': float64
'pt_ENDO': float64
'pt_FLA_EREPRO': float64
'pt_FLA_REPRO': float64
'pt_INANTH': float64
'pt_LIGCO_REV_VEG': float64
'pt_LIGSH': float64
'pt_LPCO_REV_POST': float64
'pt_LPPUB': float64
'pt_LSEN': float64
'pt_NOANTH': float64
'pt_PEX_REPRO': float64
'pt_PTH': float64
'pt_SCCO_REV': float64
'pt_SECOND_BR_REPRO': float64
'pt_SLCO_REV': float64
'pt_SPKF': float64
'pt_SLLT_CODE': float64
----------------------------------------
Row fields:
'locus': locus<GRCh37>
'alleles': array<str>
'rsid': str
'cm_position': float64
----------------------------------------
Entry fields:
'GT': call
----------------------------------------
Column key: ['s']
Row key: ['locus', 'alleles']
----------------------------------------
|
Apache-2.0
|
notebooks/organism/rice/rg-export-usage.ipynb
|
tomwhite/gwas-analysis
|
Pandas Sample data contains phenotypes prefixed by `pt_` and `s` (sample_id) in the MatrixTable matches to the `s` in this table, as does the order:
|
pd.read_csv(path / 'rg-3k-gwas-export.cols.csv').head()
|
_____no_output_____
|
Apache-2.0
|
notebooks/organism/rice/rg-export-usage.ipynb
|
tomwhite/gwas-analysis
|
Variant data shouldn't be needed for much, but it's here:
|
pd.read_csv(path / 'rg-3k-gwas-export.rows.csv.gz').head()
|
_____no_output_____
|
Apache-2.0
|
notebooks/organism/rice/rg-export-usage.ipynb
|
tomwhite/gwas-analysis
|
Zarr Call data (dense and mean imputed in this case) can be sliced from a zarr array:
|
gt = zarr.open(str(path / 'rg-3k-gwas-export.calls.zarr'), mode='r')
# Get calls for 10 variants and 5 samples
gt[5:15, 5:10]
|
_____no_output_____
|
Apache-2.0
|
notebooks/organism/rice/rg-export-usage.ipynb
|
tomwhite/gwas-analysis
|
Selecting Phenotypes Pick a phenotype: - Definitions are in https://s3-ap-southeast-1.amazonaws.com/oryzasnp-atcg-irri-org/3kRG-phenotypes/3kRG_PhenotypeData_v20170411.xlsx - The ">2007 Dictionary" sheet- Choose one with low sparsity
|
df = pd.read_csv(path / 'rg-3k-gwas-export.cols.csv')
df.info()
# First 1k variants with samples having data for this phenotype
mask = df['pt_FLA_REPRO'].notnull()
gtp = gt[:1000][:,mask]
gtp.shape, gtp.dtype
|
_____no_output_____
|
Apache-2.0
|
notebooks/organism/rice/rg-export-usage.ipynb
|
tomwhite/gwas-analysis
|
PageRank Performance Benchmarking Skip notebook testThis notebook benchmarks performance of running PageRank within cuGraph against NetworkX. NetworkX contains several implementations of PageRank. This benchmark will compare cuGraph versus the defaukt Nx implementation as well as the SciPy versionNotebook Credits Original Authors: Bradley Rees Last Edit: 08/16/2020 RAPIDS Versions: 0.15Test Hardware GV100 32G, CUDA 10,0 Intel(R) Core(TM) CPU i7-7800X @ 3.50GHz 32GB system memory Test Data| File Name | Num of Vertices | Num of Edges ||:---------------------- | --------------: | -----------: || preferentialAttachment | 100,000 | 999,970 || caidaRouterLevel | 192,244 | 1,218,132 || coAuthorsDBLP | 299,067 | 1,955,352 || dblp-2010 | 326,186 | 1,615,400 || citationCiteseer | 268,495 | 2,313,294 || coPapersDBLP | 540,486 | 30,491,458 || coPapersCiteseer | 434,102 | 32,073,440 || as-Skitter | 1,696,415 | 22,190,596 | Timing What is not timed: Reading the dataWhat is timmed: (1) creating a Graph, (2) running PageRankThe data file is read in once for all flavors of PageRank. Each timed block will craete a Graph and then execute the algorithm. The results of the algorithm are not compared. If you are interested in seeing the comparison of results, then please see PageRank in the __notebooks__ repo. NOTICE_You must have run the __dataPrep__ script prior to running this notebook so that the data is downloaded_See the README file in this folder for a discription of how to get the data Now load the required libraries
|
# Import needed libraries
import gc
import time
import rmm
import cugraph
import cudf
# NetworkX libraries
import networkx as nx
from scipy.io import mmread
try:
import matplotlib
except ModuleNotFoundError:
os.system('pip install matplotlib')
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
|
_____no_output_____
|
Apache-2.0
|
notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb
|
hlinsen/cugraph
|
Define the test data
|
# Test File
data = {
'preferentialAttachment' : './data/preferentialAttachment.mtx',
'caidaRouterLevel' : './data/caidaRouterLevel.mtx',
'coAuthorsDBLP' : './data/coAuthorsDBLP.mtx',
'dblp' : './data/dblp-2010.mtx',
'citationCiteseer' : './data/citationCiteseer.mtx',
'coPapersDBLP' : './data/coPapersDBLP.mtx',
'coPapersCiteseer' : './data/coPapersCiteseer.mtx',
'as-Skitter' : './data/as-Skitter.mtx'
}
|
_____no_output_____
|
Apache-2.0
|
notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb
|
hlinsen/cugraph
|
Define the testing functions
|
# Data reader - the file format is MTX, so we will use the reader from SciPy
def read_mtx_file(mm_file):
print('Reading ' + str(mm_file) + '...')
M = mmread(mm_file).asfptype()
return M
# CuGraph PageRank
def cugraph_call(M, max_iter, tol, alpha):
gdf = cudf.DataFrame()
gdf['src'] = M.row
gdf['dst'] = M.col
print('\tcuGraph Solving... ')
t1 = time.time()
# cugraph Pagerank Call
G = cugraph.DiGraph()
G.from_cudf_edgelist(gdf, source='src', destination='dst', renumber=False)
df = cugraph.pagerank(G, alpha=alpha, max_iter=max_iter, tol=tol)
t2 = time.time() - t1
return t2
# Basic NetworkX PageRank
def networkx_call(M, max_iter, tol, alpha):
nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}
for nnz in range(M.getnnz()):
nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]
for nnz in range(M.getnnz()):
M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])
M = M.tocsr()
if M is None:
raise TypeError('Could not read the input graph')
if M.shape[0] != M.shape[1]:
raise TypeError('Shape is not square')
# should be autosorted, but check just to make sure
if not M.has_sorted_indices:
print('sort_indices ... ')
M.sort_indices()
z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}
print('\tNetworkX Solving... ')
# start timer
t1 = time.time()
Gnx = nx.DiGraph(M)
pr = nx.pagerank(Gnx, alpha, z, max_iter, tol)
t2 = time.time() - t1
return t2
# SciPy PageRank
def networkx_scipy_call(M, max_iter, tol, alpha):
nnz_per_row = {r: 0 for r in range(M.get_shape()[0])}
for nnz in range(M.getnnz()):
nnz_per_row[M.row[nnz]] = 1 + nnz_per_row[M.row[nnz]]
for nnz in range(M.getnnz()):
M.data[nnz] = 1.0/float(nnz_per_row[M.row[nnz]])
M = M.tocsr()
if M is None:
raise TypeError('Could not read the input graph')
if M.shape[0] != M.shape[1]:
raise TypeError('Shape is not square')
# should be autosorted, but check just to make sure
if not M.has_sorted_indices:
print('sort_indices ... ')
M.sort_indices()
z = {k: 1.0/M.shape[0] for k in range(M.shape[0])}
# SciPy Pagerank Call
print('\tSciPy Solving... ')
t1 = time.time()
Gnx = nx.DiGraph(M)
pr = nx.pagerank_scipy(Gnx, alpha, z, max_iter, tol)
t2 = time.time() - t1
return t2
|
_____no_output_____
|
Apache-2.0
|
notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb
|
hlinsen/cugraph
|
Run the benchmarks
|
# arrays to capture performance gains
time_cu = []
time_nx = []
time_sp = []
perf_nx = []
perf_sp = []
names = []
# init libraries by doing a simple task
v = './data/preferentialAttachment.mtx'
M = read_mtx_file(v)
trapids = cugraph_call(M, 100, 0.00001, 0.85)
del M
for k,v in data.items():
gc.collect()
# Saved the file Name
names.append(k)
# read the data
M = read_mtx_file(v)
# call cuGraph - this will be the baseline
trapids = cugraph_call(M, 100, 0.00001, 0.85)
time_cu.append(trapids)
# Now call NetworkX
tn = networkx_call(M, 100, 0.00001, 0.85)
speedUp = (tn / trapids)
perf_nx.append(speedUp)
time_nx.append(tn)
# Now call SciPy
tsp = networkx_scipy_call(M, 100, 0.00001, 0.85)
speedUp = (tsp / trapids)
perf_sp.append(speedUp)
time_sp.append(tsp)
print("cuGraph (" + str(trapids) + ") Nx (" + str(tn) + ") SciPy (" + str(tsp) + ")" )
del M
|
_____no_output_____
|
Apache-2.0
|
notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb
|
hlinsen/cugraph
|
plot the output
|
%matplotlib inline
plt.figure(figsize=(10,8))
bar_width = 0.35
index = np.arange(len(names))
_ = plt.bar(index, perf_nx, bar_width, color='g', label='vs Nx')
_ = plt.bar(index + bar_width, perf_sp, bar_width, color='b', label='vs SciPy')
plt.xlabel('Datasets')
plt.ylabel('Speedup')
plt.title('PageRank Performance Speedup')
plt.xticks(index + (bar_width / 2), names)
plt.xticks(rotation=90)
# Text on the top of each barplot
for i in range(len(perf_nx)):
plt.text(x = (i - 0.55) + bar_width, y = perf_nx[i] + 25, s = round(perf_nx[i], 1), size = 12)
for i in range(len(perf_sp)):
plt.text(x = (i - 0.1) + bar_width, y = perf_sp[i] + 25, s = round(perf_sp[i], 1), size = 12)
plt.legend()
plt.show()
|
_____no_output_____
|
Apache-2.0
|
notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb
|
hlinsen/cugraph
|
Dump the raw stats
|
perf_nx
perf_sp
time_cu
time_nx
time_sp
|
_____no_output_____
|
Apache-2.0
|
notebooks/cugraph_benchmarks/pagerank_benchmark.ipynb
|
hlinsen/cugraph
|
My Notebook 2
|
import os
print("I am notebook 2")
|
_____no_output_____
|
BSD-3-Clause
|
nbcollection/tests/data/my_notebooks/sub_path1/notebook2.ipynb
|
jonathansick/nbcollection
|
We'll continue to make use of the fuel economy dataset in this workspace.
|
fuel_econ = pd.read_csv('./data/fuel_econ.csv')
fuel_econ.head()
|
_____no_output_____
|
MIT
|
Matplotlib/Violin_and_Box_Plot_Practice.ipynb
|
iamleeg/AIPND
|
**Task**: What is the relationship between the size of a car and the size of its engine? The cars in this dataset are categorized into one of five different vehicle classes based on size. Starting from the smallest, they are: {Minicompact Cars, Subcompact Cars, Compact Cars, Midsize Cars, and Large Cars}. The vehicle classes can be found in the 'VClass' variable, while the engine sizes are in the 'displ' column (in liters). **Hint**: Make sure that the order of vehicle classes makes sense in your plot!
|
# YOUR CODE HERE
car_classes = ['Minicompact Cars', 'Subcompact Cars', 'Compact Cars', 'Midsize Cars', 'Large Cars']
vclasses = pd.api.types.CategoricalDtype(ordered = True, categories = car_classes)
fuel_econ['VClass'] = fuel_econ['VClass'].astype(vclasses)
sb.violinplot(data = fuel_econ, x = 'VClass', y = 'displ')
plt.xticks(rotation = 15)
# run this cell to check your work against ours
violinbox_solution_1()
|
I used a violin plot to depict the data in this case; you might have chosen a box plot instead. One of the interesting things about the relationship between variables is that it isn't consistent. Compact cars tend to have smaller engine sizes than the minicompact and subcompact cars, even though those two vehicle sizes are smaller. The box plot would make it easier to see that the median displacement for the two smallest vehicle classes is greater than the third quartile of the compact car class.
|
MIT
|
Matplotlib/Violin_and_Box_Plot_Practice.ipynb
|
iamleeg/AIPND
|
Langmuir-enhanced entrainmentThis notebook reproduces Fig. 15 of [Li et al., 2019](https://doi.org/10.1029/2019MS001810).
|
import sys
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
sys.path.append("../../../gotmtool")
from gotmtool import *
def plot_hLL_dpedt(hLL, dpedt, casename_list, ax=None, xlabel_on=True):
if ax is None:
ax = plt.gca()
idx_WD05 = [('WD05' in casename) for casename in casename_list]
idx_WD08 = [('WD08' in casename) for casename in casename_list]
idx_WD10 = [('WD10' in casename) for casename in casename_list]
b0_str = [casename[2:4] for casename in casename_list]
b0 = np.array([float(tmp[0])*100 if 'h' in tmp else float(tmp) for tmp in b0_str])
b0_min = b0.min()
b0_max = b0.max()
ax.plot(hLL, dpedt, color='k', linewidth=1, linestyle=':', zorder=1)
im = ax.scatter(hLL[idx_WD05], dpedt[idx_WD05], c=b0[idx_WD05], marker='d', edgecolors='k',
linewidth=1, zorder=2, label='$U_{10}=5$ m s$^{-1}$', cmap='bone_r', vmin=b0_min, vmax=b0_max)
ax.scatter(hLL[idx_WD08], dpedt[idx_WD08], c=b0[idx_WD08], marker='s', edgecolors='k',
linewidth=1, zorder=2, label='$U_{10}=8$ m s$^{-1}$', cmap='bone_r', vmin=b0_min, vmax=b0_max)
ax.scatter(hLL[idx_WD10], dpedt[idx_WD10], c=b0[idx_WD10], marker='^', edgecolors='k',
linewidth=1, zorder=2, label='$U_{10}=10$ m s$^{-1}$', cmap='bone_r', vmin=b0_min, vmax=b0_max)
ax.legend(loc='upper left')
# add colorbar
ax_inset = inset_axes(ax, width="30%", height="3%", loc='lower right',
bbox_to_anchor=(-0.05, 0.1, 1, 1),
bbox_transform=ax.transAxes,
borderpad=0,)
cb = plt.colorbar(im, cax=ax_inset, orientation='horizontal', shrink=0.35,
ticks=[5, 100, 300, 500])
cb.ax.set_xticklabels(['-5','-100','-300','-500'])
ax.text(0.75, 0.2, '$Q_0$ (W m$^{-2}$)', color='black', transform=ax.transAxes,
fontsize=10, va='top', ha='left')
# get axes ratio
ll, ur = ax.get_position() * plt.gcf().get_size_inches()
width, height = ur - ll
axes_ratio = height / width
# add arrow and label
add_arrow(ax, 0.6, 0.2, 0.3, 0.48, axes_ratio, color='gray', text='Increasing Convection')
add_arrow(ax, 0.3, 0.25, -0.2, 0.1, axes_ratio, color='black', text='Increasing Langmuir')
add_arrow(ax, 0.65, 0.75, -0.25, 0.01, axes_ratio, color='black', text='Increasing Langmuir')
ax.set_xscale('log')
ax.set_yscale('log')
if xlabel_on:
ax.set_xlabel('$h/\kappa L$', fontsize=14)
ax.set_ylabel('$d\mathrm{PE}/dt$', fontsize=14)
ax.set_xlim([3e-3, 4e1])
ax.set_ylim([2e-4, 5e-2])
# set the tick labels font
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(14)
def plot_hLL_R(hLL, R, colors, legend_list, ax=None, xlabel_on=True):
if ax is None:
ax = plt.gca()
ax.axhline(y=1, linewidth=1, color='black')
nm = R.shape[0]
for i in np.arange(nm):
ax.scatter(hLL, R[i,:], color=colors[i], edgecolors='k', linewidth=0.5, zorder=10)
ax.set_xscale('log')
ax.set_xlim([3e-3, 4e1])
if xlabel_on:
ax.set_xlabel('$h/L_L$', fontsize=14)
ax.set_ylabel('$R$', fontsize=14)
# set the tick labels font
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
label.set_fontsize(14)
# legend
if nm > 1:
xshift = 0.2 + 0.05*(11-nm)
xx = np.arange(nm)+1
xx = xx*0.06+xshift
yy = np.ones(xx.size)*0.1
for i in np.arange(nm):
ax.text(xx[i], yy[i], legend_list[i], color='black', transform=ax.transAxes,
fontsize=12, rotation=45, va='bottom', ha='left')
ax.scatter(xx[i], 0.07, s=60, color=colors[i], edgecolors='k', linewidth=1, transform=ax.transAxes)
def add_arrow(ax, x, y, dx, dy, axes_ratio, color='black', text=None):
ax.arrow(x, y, dx, dy, width=0.006, color=color, transform=ax.transAxes)
if text is not None:
dl = np.sqrt(dx**2+dy**2)
xx = x + 0.5*dx + dy/dl*0.06
yy = y + 0.5*dy - dx/dl*0.06
angle = np.degrees(np.arctan(dy/dx*axes_ratio))
ax.text(xx, yy, text, color=color, transform=ax.transAxes, fontsize=11,
rotation=angle, va='center', ha='center')
|
_____no_output_____
|
MIT
|
examples/Entrainment-LF17/plot_Entrainment-LF17.ipynb
|
jithuraju1290/gotmtool
|
Load LF17 data
|
# load LF17 data
lf17_data = np.load('LF17_dPEdt.npz')
us0 = lf17_data['us0']
b0 = lf17_data['b0']
ustar = lf17_data['ustar']
hb = lf17_data['hb']
dpedt = lf17_data['dpedt']
casenames = lf17_data['casenames']
ncase = len(casenames)
# get parameter h/L_L= w*^3/u*^2/u^s(0)
inds = us0==0
us0[inds] = np.nan
hLL = b0*hb/ustar**2/us0
|
_____no_output_____
|
MIT
|
examples/Entrainment-LF17/plot_Entrainment-LF17.ipynb
|
jithuraju1290/gotmtool
|
Compute the rate of change in potential energy in GOTM runs
|
turbmethods = [
'GLS-C01A',
'KPP-CVMix',
'KPPLT-VR12',
'KPPLT-LF17',
]
ntm = len(turbmethods)
cmap = cm.get_cmap('rainbow')
if ntm == 1:
colors = ['gray']
else:
colors = cmap(np.linspace(0,1,ntm))
m = Model(name='Entrainment-LF17', environ='../../.gotm_env.yaml')
gotmdir = m.environ['gotmdir_run']+'/'+m.name
print(gotmdir)
# Coriolis parameter (s^{-1})
f = 4*np.pi/86400*np.sin(np.pi/4)
# Inertial period (s)
Ti = 2*np.pi/f
# get dPEdt from GOTM run
rdpedt = np.zeros([ntm, ncase])
for i in np.arange(ntm):
print(turbmethods[i])
for j in np.arange(ncase):
sim = Simulation(path=gotmdir+'/'+casenames[j]+'/'+turbmethods[i])
var_gotm = sim.load_data().Epot
epot_gotm = var_gotm.data.squeeze()
dtime = var_gotm.time - var_gotm.time[0]
time_gotm = (dtime.dt.days*86400.+dtime.dt.seconds).data
# starting index for the last inertial period
t0_gotm = time_gotm[-1]-Ti
tidx0_gotm = np.argmin(np.abs(time_gotm-t0_gotm))
# linear fit
xx_gotm = time_gotm[tidx0_gotm:]-time_gotm[tidx0_gotm]
yy_gotm = epot_gotm[tidx0_gotm:]-epot_gotm[tidx0_gotm]
slope_gotm, intercept_gotm, r_value_gotm, p_value_gotm, std_err_gotm = stats.linregress(xx_gotm,yy_gotm)
rdpedt[i,j] = slope_gotm/dpedt[j]
fig, axarr = plt.subplots(2, 1, sharex='col')
fig.set_size_inches(6, 7)
plt.subplots_adjust(left=0.15, right=0.95, bottom=0.09, top=0.95, hspace=0.1)
plot_hLL_dpedt(hLL, dpedt, casenames, ax=axarr[0])
plot_hLL_R(hLL, rdpedt, colors, turbmethods, ax=axarr[1])
axarr[0].text(0.04, 0.14, '(a)', color='black', transform=axarr[0].transAxes,
fontsize=14, va='top', ha='left')
axarr[1].text(0.88, 0.94, '(b)', color='black', transform=axarr[1].transAxes,
fontsize=14, va='top', ha='left')
|
_____no_output_____
|
MIT
|
examples/Entrainment-LF17/plot_Entrainment-LF17.ipynb
|
jithuraju1290/gotmtool
|
Statistics **Quick intro to the following packages**- `hepstats`.I will not discuss here the `pyhf` package, which is very niche.Please refer to the [GitHub repository](https://github.com/scikit-hep/pyhf) or related material at https://scikit-hep.org/resources. **`hepstats` - statistics tools and utilities**The package contains 2 submodules:- `hypotests`: provides tools to do hypothesis tests such as discovery test and computations of upper limits or confidence intervals.- `modeling`: includes the Bayesian Block algorithm that can be used to improve the binning of histograms.Note: feel free to complement the introduction below with the several tutorials available from the [GitHub repository](https://github.com/scikit-hep/hepstats). **1. Adaptive binning determination**The Bayesian Block algorithm produces histograms that accurately represent the underlying distribution while being robust to statistical fluctuations.
|
import numpy as np
import matplotlib.pyplot as plt
from hepstats.modeling import bayesian_blocks
data = np.append(np.random.laplace(size=10000), np.random.normal(5., 1., size=15000))
bblocks = bayesian_blocks(data)
plt.hist(data, bins=1000, label='Fine Binning', density=True)
plt.hist(data, bins=bblocks, label='Bayesian Blocks', histtype='step', linewidth=2, density=True)
plt.legend(loc=2);
|
_____no_output_____
|
BSD-3-Clause
|
05-statistics.ipynb
|
eduardo-rodrigues/2020-03-03_DESY_Scikit-HEP_HandsOn
|
Tirmzi Analysisn=1000 m+=1000 nm-=120 istep= 4 min=150 max=700
|
import sys
sys.path
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import signal
ls
import capsol.newanalyzecapsol as ac
ac.get_gridparameters
import glob
folders = glob.glob("FortranOutputTest/*/")
folders
all_data= dict()
for folder in folders:
params = ac.get_gridparameters(folder + 'capsol.in')
data = ac.np.loadtxt(folder + 'Z-U.dat')
process_data = ac.process_data(params, data, smoothing=False, std=5*10**-9)
all_data[folder]= (process_data)
all_params= dict()
for folder in folders:
params=ac.get_gridparameters(folder + 'capsol.in')
all_params[folder]= (params)
all_data
all_data.keys()
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:
data=all_data[key]
thickness =all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')
plt.title('C v. Z for 1nm thick sample')
plt.ylabel("C(m)")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("C' v. Z for 1nm thick sample 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:
data=all_data[key]
thickness =all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')
plt.title('C v. Z for 10nm thick sample')
plt.ylabel("C(m)")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("C' v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:
data=all_data[key]
thickness =all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')
plt.title('C v. Z for 100nm sample')
plt.ylabel("C(m)")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("C' v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:
data=all_data[key]
thickness =all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
plt.plot(data['z'], data['c'], label= f'{rtip} nm, {er}, {thickness} nm')
plt.title('C v. Z for 500nm sample')
plt.ylabel("C(m)")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("C' v. Z for varying sample thickness, 06-28-2021.png")
|
No handles with labels found to put in legend.
|
MIT
|
data/Output-Python/Tirmzi_istep4-Copy2.ipynb
|
maroniea/xsede-spm
|
cut off last experiment because capacitance was off the scale
|
for params in all_params.values():
print(params['Thickness_sample'])
print(params['m-'])
all_params
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(4,-3)
plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Cz vs. Z for 1.0nm')
plt.ylabel("Cz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Cz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(4,-3)
plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Cz vs. Z for 10.0nm')
plt.ylabel("Cz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Cz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(4,-3)
plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Cz vs. Z for 100.0nm')
plt.ylabel("Cz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Cz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(4,-3)
plt.plot(data['z'][s], data['cz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Cz vs. Z for 500.0nm')
plt.ylabel("Cz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Cz v. Z for varying sample thickness, 06-28-2021.png")
hoepker_data= np.loadtxt("Default Dataset (2).csv" , delimiter= ",")
hoepker_data
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(5,-5)
plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Czz vs. Z for 1.0nm')
plt.ylabel("Czz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Czz v. Z for varying sample thickness, 06-28-2021.png")
params
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(5,-5)
plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Czz vs. Z for 10.0nm')
plt.ylabel("Czz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Czz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(5,-5)
plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Czz vs. Z for 100.0nm')
plt.ylabel("Czz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Czz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(5,-5)
plt.plot(data['z'][s], data['czz'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Czz vs. Z for 500.0 nm')
plt.ylabel("Czz")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Czz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 1.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(8,-8)
plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('alpha vs. Z for 1.0nm')
plt.ylabel("$\\alpha$")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Alpha v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 10.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(8,-8)
plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Alpha vs. Z for 10.0 nm')
plt.ylabel("$\\alpha$")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Czz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 100.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(8,-8)
plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Alpha vs. Z for 100.0nm')
plt.ylabel("$\\alpha$")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Czz v. Z for varying sample thickness, 06-28-2021.png")
for key in {key: params for key, params in all_params.items() if params['Thickness_sample'] == 500.0}:
data=all_data[key]
thickness=all_params[key]['Thickness_sample']
rtip= all_params[key]['Rtip']
er=all_params[key]['eps_r']
s=slice(8,-8)
plt.plot(data['z'][s], data['alpha'][s], label=f'{rtip} nm, {er}, {thickness} nm' )
plt.title('Alpha vs. Z for 500.0nm')
plt.ylabel("$\\alpha$")
plt.xlabel("Z(m)")
plt.legend()
plt.savefig("Czz v. Z for varying sample thickness, 06-28-2021.png")
data
from scipy.optimize import curve_fit
def Cz_model(z, a, n, b,):
return(a*z**n + b)
all_data.keys()
data= all_data['capsol-calc\\0001-capsol\\']
z= data['z'][1:-1]
cz= data['cz'][1:-1]
popt, pcov= curve_fit(Cz_model, z, cz, p0=[cz[0]*z[0], -1, 0])
a=popt[0]
n=popt[1]
b=popt[2]
std_devs= np.sqrt(pcov.diagonal())
sigma_a = std_devs[0]
sigma_n = std_devs[1]
model_output= Cz_model(z, a, n, b)
rmse= np.sqrt(np.mean((cz - model_output)**2))
f"a= {a} ยฑ {sigma_a}"
f"n= {n}ยฑ {sigma_n}"
model_output
"Root Mean Square Error"
rmse/np.mean(-cz)
|
_____no_output_____
|
MIT
|
data/Output-Python/Tirmzi_istep4-Copy2.ipynb
|
maroniea/xsede-spm
|
Q-learning - Initialize $V(s)$ arbitrarily- Repeat for each episode- Initialize s- Repeat (for each step of episode)- - $\alpha \leftarrow$ action given by $\pi$ for $s$- - Take action a, observe reward r, and next state s'- - $V(s) \leftarrow V(s) + \alpha [r = \gamma V(s') - V(s)]$ - - $s \leftarrow s'$- until $s$ is terminal
|
import td
import scipy as sp
ฮฑ = 0.05
ฮณ = 0.1
td_learning = td.TD(ฮฑ, ฮณ)
|
_____no_output_____
|
MIT
|
notebooks/TD Learning Black Scholes.ipynb
|
FinTechies/HedgingRL
|
Black Scholes $${\displaystyle d_{1}={\frac {1}{\sigma {\sqrt {T-t}}}}\left[\ln \left({\frac {S_{t}}{K}}\right)+(r-q+{\frac {1}{2}}\sigma ^{2})(T-t)\right]}$$ $${\displaystyle C(S_{t},t)=e^{-r(T-t)}[FN(d_{1})-KN(d_{2})]\,}$$ $${\displaystyle d_{2}=d_{1}-\sigma {\sqrt {T-t}}={\frac {1}{\sigma {\sqrt {T-t}}}}\left[\ln \left({\frac {S_{t}}{K}}\right)+(r-q-{\frac {1}{2}}\sigma ^{2})(T-t)\right]}$$
|
d_1 = lambda ฯ, T, t, S, K: 1. / ฯ / np.sqrt(T - t) * (np.log(S / K) + 0.5 * (ฯ ** 2) * (T-t))
d_2 = lambda ฯ, T, t, S, K: 1. / ฯ / np.sqrt(T - t) * (np.log(S / K) - 0.5 * (ฯ ** 2) * (T-t))
call = lambda ฯ, T, t, S, K: S * sp.stats.norm.cdf( d_1(ฯ, T, t, S, K) ) - K * sp.stats.norm.cdf( d_2(ฯ, T, t, S, K) )
plt.plot(np.linspace(0.1, 4., 100), call(1., 1., .9, np.linspace(0.1, 4., 100), 1.))
d_1(1., 1., 0., 1.9, 1)
plt.plot(d_1(1., 1., 0., np.linspace(0.1, 2.9, 10), 1))
plt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.2, np.linspace(0.01, 1.9, 100), 1)))
plt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.6, np.linspace(0.01, 1.9, 100), 1)))
plt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.9, np.linspace(0.01, 1.9, 100), 1)))
plt.plot(np.linspace(0.01, 1.9, 100), sp.stats.norm.cdf(d_1(1., 1., 0.99, np.linspace(0.01, 1.9, 100), 1)))
def iterate_series(n=1000, S0 = 1):
while True:
r = np.random.randn((n))
S = np.cumsum(r) + S0
yield S, r
def iterate_world(n=1000, S0=1, N=5):
for (s, r) in take(N, iterate_series(n=n, S0=S0)):
t, t_0 = 0, 0
for t in np.linspace(0, len(s)-1, 100):
r = s[int(t)] / s[int(t_0)]
yield r, s[int(t)]
t_0 = t
from cytoolz import take
import gym
import gym_bs
from test_cem_future import *
import pandas as pd
import numpy as np
# df.iloc[3] = (0.2, 1, 3)
df
rwd, df, agent = noisy_evaluation(np.array([0.1, 0, 0]))
rwd
df
agent;
env.observation_space
|
_____no_output_____
|
MIT
|
notebooks/TD Learning Black Scholes.ipynb
|
FinTechies/HedgingRL
|
Plotting with Matplotlib IPython works with the [Matplotlib](http://matplotlib.org/) plotting library, which integrates Matplotlib with IPython's display system and event loop handling. matplotlib mode To make plots using Matplotlib, you must first enable IPython's matplotlib mode.To do this, run the `%matplotlib` magic command to enable plotting in the current Notebook.This magic takes an optional argument that specifies which Matplotlib backend should be used. Most of the time, in the Notebook, you will want to use the `inline` backend, which will embed plots inside the Notebook:
|
%matplotlib inline
|
_____no_output_____
|
BSD-3-Clause
|
001-Jupyter/001-Tutorials/003-IPython-in-Depth/examples/IPython Kernel/Plotting in the Notebook.ipynb
|
willirath/jupyter-jsc-notebooks
|
You can also use Matplotlib GUI backends in the Notebook, such as the Qt backend (`%matplotlib qt`). This will use Matplotlib's interactive Qt UI in a floating window to the side of your browser. Of course, this only works if your browser is running on the same system as the Notebook Server. You can always call the `display` function to paste figures into the Notebook document. Making a simple plot With matplotlib enabled, plotting should just work.
|
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 3*np.pi, 500)
plt.plot(x, np.sin(x**2))
plt.title('A simple chirp');
|
_____no_output_____
|
BSD-3-Clause
|
001-Jupyter/001-Tutorials/003-IPython-in-Depth/examples/IPython Kernel/Plotting in the Notebook.ipynb
|
willirath/jupyter-jsc-notebooks
|
These images can be resized by dragging the handle in the lower right corner. Double clicking will return them to their original size. One thing to be aware of is that by default, the `Figure` object is cleared at the end of each cell, so you will need to issue all plotting commands for a single figure in a single cell. Loading Matplotlib demos with %load IPython's `%load` magic can be used to load any Matplotlib demo by its URL:
|
# %load http://matplotlib.org/mpl_examples/showcase/integral_demo.py
"""
Plot demonstrating the integral as the area under a curve.
Although this is a simple example, it demonstrates some important tweaks:
* A simple line plot with custom color and line width.
* A shaded region created using a Polygon patch.
* A text label with mathtext rendering.
* figtext calls to label the x- and y-axes.
* Use of axis spines to hide the top and right spines.
* Custom tick placement and labels.
"""
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon
def func(x):
return (x - 3) * (x - 5) * (x - 7) + 85
a, b = 2, 9 # integral limits
x = np.linspace(0, 10)
y = func(x)
fig, ax = plt.subplots()
plt.plot(x, y, 'r', linewidth=2)
plt.ylim(bottom=0)
# Make the shaded region
ix = np.linspace(a, b)
iy = func(ix)
verts = [(a, 0)] + list(zip(ix, iy)) + [(b, 0)]
poly = Polygon(verts, facecolor='0.9', edgecolor='0.5')
ax.add_patch(poly)
plt.text(0.5 * (a + b), 30, r"$\int_a^b f(x)\mathrm{d}x$",
horizontalalignment='center', fontsize=20)
plt.figtext(0.9, 0.05, '$x$')
plt.figtext(0.1, 0.9, '$y$')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.set_xticks((a, b))
ax.set_xticklabels(('$a$', '$b$'))
ax.set_yticks([])
plt.show()
|
_____no_output_____
|
BSD-3-Clause
|
001-Jupyter/001-Tutorials/003-IPython-in-Depth/examples/IPython Kernel/Plotting in the Notebook.ipynb
|
willirath/jupyter-jsc-notebooks
|
Matplotlib 1.4 introduces an interactive backend for use in the notebook,called 'nbagg'. You can enable this with `%matplotlib notebook`.With this backend, you will get interactive panning and zooming of matplotlib figures in your browser.
|
%matplotlib widget
plt.figure()
x = np.linspace(0, 5 * np.pi, 1000)
for n in range(1, 4):
plt.plot(np.sin(n * x))
plt.show()
|
_____no_output_____
|
BSD-3-Clause
|
001-Jupyter/001-Tutorials/003-IPython-in-Depth/examples/IPython Kernel/Plotting in the Notebook.ipynb
|
willirath/jupyter-jsc-notebooks
|
Let's start by importing the libraries that we need for this exercise.
|
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib
from sklearn.model_selection import train_test_split
#matplotlib settings
matplotlib.rcParams['xtick.major.size'] = 7
matplotlib.rcParams['xtick.labelsize'] = 'x-large'
matplotlib.rcParams['ytick.major.size'] = 7
matplotlib.rcParams['ytick.labelsize'] = 'x-large'
matplotlib.rcParams['xtick.top'] = False
matplotlib.rcParams['ytick.right'] = False
matplotlib.rcParams['ytick.direction'] = 'in'
matplotlib.rcParams['xtick.direction'] = 'in'
matplotlib.rcParams['font.size'] = 15
matplotlib.rcParams['figure.figsize'] = [7,7]
#We need the astroml library to fetch the photometric datasets of sdss qsos and stars
pip install astroml
from astroML.datasets import fetch_dr7_quasar
from astroML.datasets import fetch_sdss_sspp
quasars = fetch_dr7_quasar()
stars = fetch_sdss_sspp()
# Data procesing taken from
#https://www.astroml.org/book_figures/chapter9/fig_star_quasar_ROC.html by Jake Van der Plus
# stack colors into matrix X
Nqso = len(quasars)
Nstars = len(stars)
X = np.empty((Nqso + Nstars, 4), dtype=float)
X[:Nqso, 0] = quasars['mag_u'] - quasars['mag_g']
X[:Nqso, 1] = quasars['mag_g'] - quasars['mag_r']
X[:Nqso, 2] = quasars['mag_r'] - quasars['mag_i']
X[:Nqso, 3] = quasars['mag_i'] - quasars['mag_z']
X[Nqso:, 0] = stars['upsf'] - stars['gpsf']
X[Nqso:, 1] = stars['gpsf'] - stars['rpsf']
X[Nqso:, 2] = stars['rpsf'] - stars['ipsf']
X[Nqso:, 3] = stars['ipsf'] - stars['zpsf']
y = np.zeros(Nqso + Nstars, dtype=int)
y[:Nqso] = 1
X = X/np.max(X, axis=0)
# split into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = 0.9)
#Now let's build a simple Sequential model in which fully connected layers come after one another
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(), #this flattens input
tf.keras.layers.Dense(128, activation = "relu"),
tf.keras.layers.Dense(64, activation = "relu"),
tf.keras.layers.Dense(32, activation = "relu"),
tf.keras.layers.Dense(32, activation = "relu"),
tf.keras.layers.Dense(1, activation="sigmoid")
])
model.compile(optimizer='adam', loss='binary_crossentropy')
history = model.fit(X_train, y_train, validation_data = (X_test, y_test), batch_size = 32, epochs=20, verbose = 1)
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(loss))
plt.plot(epochs, loss, lw = 5, label='Training loss')
plt.plot(epochs, val_loss, lw = 5, label='validation loss')
plt.title('Loss')
plt.legend(loc=0)
plt.show()
prob = model.predict_proba(X_test) #model probabilities
from sklearn.metrics import confusion_matrix
from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_test, prob)
plt.loglog(fpr, tpr, lw = 4)
plt.xlabel('false positive rate')
plt.ylabel('true positive rate')
plt.xlim(0.0, 0.15)
plt.ylim(0.6, 1.01)
plt.show()
plt.plot(thresholds, tpr, lw = 4)
plt.plot(thresholds, fpr, lw = 4)
plt.xlim(0,1)
plt.yscale("log")
plt.show()
#plt.xlabel('false positive rate')
#plt.ylabel('true positive rate')
##plt.xlim(0.0, 0.15)
#plt.ylim(0.6, 1.01)
#Now let's look at the confusion matrix
y_pred = model.predict(X_test)
z_pred = np.zeros(y_pred.shape[0], dtype = int)
mask = np.where(y_pred>.5)[0]
z_pred[mask] = 1
confusion_matrix(y_test, z_pred.astype(int))
import os, signal
os.kill(os.getpid(), signal.SIGKILL)
|
_____no_output_____
|
MIT
|
day2/nn_qso_finder.ipynb
|
mjvakili/MLcourse
|
df1.query('age == 10')
You can also achieve this result via the traditional filtering method.
filter_1 = df['Mon'] > df['Tues']
df[filter_1]
If needed you can also use an environment variable to filter your data.
Make sure to put an "@" sign in front of your variable within the string.
dinner_limit=120
df.query('Thurs > @dinner_limit')
|
_____no_output_____
|
MIT
|
PandasQureys.ipynb
|
nealonleo9/SQL
|
|
Udacity PyTorch Scholarship Final Lab Challenge Guide **A hands-on guide to get 90% + accuracy and complete the challenge** **By [Soumya Ranjan Behera](https://www.linkedin.com/in/soumya044)** This Tutorial will be divided into Two Parts, [1. Model Building and Training](https://www.kaggle.com/soumya044/udacity-pytorch-final-lab-guide-part-1/) [2. Submit in Udcaity's Workspace for evaluation](https://www.kaggle.com/soumya044/udacity-pytorch-final-lab-guide-part-2/) **Note:** This tutorial is like a template or guide for newbies to overcome the fear of the final lab challenge. My intent is not to promote plagiarism or any means of cheating. Users are encourage to take this tutorial as a baseline and build their own better model. Cheers! **Fork this Notebook and Run it from Top-To-Bottom Step by Step** Part 1: Build and Train a Model **Credits:** The dataset credit goes to [Lalu Erfandi Maula Yusnu](https://www.kaggle.com/nunenuh) 1. Import Data set and visualiza some data
|
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
print(os.listdir("../input/"))
# Any results you write to the current directory are saved as output.
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**Import some visualization Libraries**
|
import matplotlib.pyplot as plt
%matplotlib inline
import cv2
# Set Train and Test Directory Variables
TRAIN_DATA_DIR = "../input/flower_data/flower_data/train/"
VALID_DATA_DIR = "../input/flower_data/flower_data/valid/"
#Visualiza Some Images of any Random Directory-cum-Class
FILE_DIR = str(np.random.randint(1,103))
print("Class Directory: ",FILE_DIR)
for file_name in os.listdir(os.path.join(TRAIN_DATA_DIR, FILE_DIR))[1:3]:
img_array = cv2.imread(os.path.join(TRAIN_DATA_DIR, FILE_DIR, file_name))
img_array = cv2.resize(img_array,(224, 224), interpolation = cv2.INTER_CUBIC)
plt.imshow(img_array)
plt.show()
print(img_array.shape)
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
2. Data Preprocessing (Image Augmentation) **Import PyTorch libraries**
|
import torch
import torchvision
from torchvision import datasets, models, transforms
import torch.nn as nn
torch.__version__
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**Note:** **Look carefully! Kaggle uses v1.0.0 while Udcaity workspace has v0.4.0 (Some issues may arise but we'll solve them)**
|
# check if CUDA is available
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...')
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**Make a Class Variable i.e a list of Target Categories (List of 102 species) **
|
# I used os.listdir() to maintain the ordering
classes = os.listdir(VALID_DATA_DIR)
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**Load and Transform (Image Augmentation)** Soucre: https://github.com/udacity/deep-learning-v2-pytorch/blob/master/convolutional-neural-networks/cifar-cnn/cifar10_cnn_augmentation.ipynb
|
# Load and transform data using ImageFolder
# VGG-16 Takes 224x224 images as input, so we resize all of them
data_transform = transforms.Compose([transforms.RandomResizedCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])])
train_data = datasets.ImageFolder(TRAIN_DATA_DIR, transform=data_transform)
test_data = datasets.ImageFolder(VALID_DATA_DIR, transform=data_transform)
# print out some data stats
print('Num training images: ', len(train_data))
print('Num test images: ', len(test_data))
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
Find more on Image Transforms using PyTorch Here (https://pytorch.org/docs/stable/torchvision/transforms.html) 3. Make a DataLoader
|
# define dataloader parameters
batch_size = 32
num_workers=0
# prepare data loaders
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,
num_workers=num_workers, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size,
num_workers=num_workers, shuffle=True)
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**Visualize Sample Images**
|
# Visualize some sample data
# obtain one batch of training images
dataiter = iter(train_loader)
images, labels = dataiter.next()
images = images.numpy() # convert images to numpy for display
# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(20):
ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])
plt.imshow(np.transpose(images[idx], (1, 2, 0)))
ax.set_title(classes[labels[idx]])
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**Here plt.imshow() clips our data into [0,....,255] range to show the images. The Warning message is due to our Transform Function. We can Ignore it.** 4. Use a Pre-Trained Model (VGG16) Here we used a VGG16. You can experiment with other models. References: https://github.com/udacity/deep-learning-v2-pytorch/blob/master/transfer-learning/Transfer_Learning_Solution.ipynb **Try More Models: ** https://pytorch.org/docs/stable/torchvision/models.html
|
# Load the pretrained model from pytorch
model = models.<ModelNameHere>(pretrained=True)
print(model)
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
We can see from above output that the last ,i.e, 6th Layer is a Fully-connected Layer with in_features=4096, out_features=1000
|
print(model.classifier[6].in_features)
print(model.classifier[6].out_features)
# The above lines work for vgg only. For other models refer to print(model) and look for last FC layer
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**Freeze Training for all 'Features Layers', Only Train Classifier Layers**
|
# Freeze training for all "features" layers
for param in model.features.parameters():
param.requires_grad = False
#For models like ResNet or Inception use the following,
# Freeze training for all "features" layers
# for _, param in model.named_parameters():
# param.requires_grad = False
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
Let's Add our own Last Layer which will have 102 out_features for 102 species
|
# VGG16
n_inputs = model.classifier[6].in_features
#Others
# n_inputs = model.fc.in_features
# add last linear layer (n_inputs -> 102 flower classes)
# new layers automatically have requires_grad = True
last_layer = nn.Linear(n_inputs, len(classes))
# VGG16
model.classifier[6] = last_layer
# Others
#model.fc = last_layer
# if GPU is available, move the model to GPU
if train_on_gpu:
model.cuda()
# check to see that your last layer produces the expected number of outputs
#VGG
print(model.classifier[6].out_features)
#Others
#print(model.fc.out_features)
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
5. Specify our Loss Function and Optimzer
|
import torch.optim as optim
# specify loss function (categorical cross-entropy)
criterion = #TODO
# specify optimizer (stochastic gradient descent) and learning rate = 0.01 or 0.001
optimizer = #TODO
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
6. Train our Model and Save necessary checkpoints
|
# Define epochs (between 50-200)
epochs = 20
# initialize tracker for minimum validation loss
valid_loss_min = np.Inf # set initial "min" to infinity
# Some lists to keep track of loss and accuracy during each epoch
epoch_list = []
train_loss_list = []
val_loss_list = []
train_acc_list = []
val_acc_list = []
# Start epochs
for epoch in range(epochs):
#adjust_learning_rate(optimizer, epoch)
# monitor training loss
train_loss = 0.0
val_loss = 0.0
###################
# train the model #
###################
# Set the training mode ON -> Activate Dropout Layers
model.train() # prepare model for training
# Calculate Accuracy
correct = 0
total = 0
# Load Train Images with Labels(Targets)
for data, target in train_loader:
if train_on_gpu:
data, target = data.cuda(), target.cuda()
# clear the gradients of all optimized variables
optimizer.zero_grad()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
if type(output) == tuple:
output, _ = output
# Calculate Training Accuracy
predicted = torch.max(output.data, 1)[1]
# Total number of labels
total += len(target)
# Total correct predictions
correct += (predicted == target).sum()
# calculate the loss
loss = criterion(output, target)
# backward pass: compute gradient of the loss with respect to model parameters
loss.backward()
# perform a single optimization step (parameter update)
optimizer.step()
# update running training loss
train_loss += loss.item()*data.size(0)
# calculate average training loss over an epoch
train_loss = train_loss/len(train_loader.dataset)
# Avg Accuracy
accuracy = 100 * correct / float(total)
# Put them in their list
train_acc_list.append(accuracy)
train_loss_list.append(train_loss)
# Implement Validation like K-fold Cross-validation
# Set Evaluation Mode ON -> Turn Off Dropout
model.eval() # Required for Evaluation/Test
# Calculate Test/Validation Accuracy
correct = 0
total = 0
with torch.no_grad():
for data, target in test_loader:
if train_on_gpu:
data, target = data.cuda(), target.cuda()
# Predict Output
output = model(data)
if type(output) == tuple:
output, _ = output
# Calculate Loss
loss = criterion(output, target)
val_loss += loss.item()*data.size(0)
# Get predictions from the maximum value
predicted = torch.max(output.data, 1)[1]
# Total number of labels
total += len(target)
# Total correct predictions
correct += (predicted == target).sum()
# calculate average training loss and accuracy over an epoch
val_loss = val_loss/len(test_loader.dataset)
accuracy = 100 * correct/ float(total)
# Put them in their list
val_acc_list.append(accuracy)
val_loss_list.append(val_loss)
# Print the Epoch and Training Loss Details with Validation Accuracy
print('Epoch: {} \tTraining Loss: {:.4f}\t Val. acc: {:.2f}%'.format(
epoch+1,
train_loss,
accuracy
))
# save model if validation loss has decreased
if val_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(
valid_loss_min,
val_loss))
# Save Model State on Checkpoint
torch.save(model.state_dict(), 'model.pt')
valid_loss_min = val_loss
# Move to next epoch
epoch_list.append(epoch + 1)
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
Load Model State from Checkpoint
|
model.load_state_dict(torch.load('model.pt'))
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
Save the whole Model (Pickling)
|
#Save/Pickle the Model
torch.save(model, 'classifier.pth')
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
7. Visualize Model Training and Validation
|
# Training / Validation Loss
plt.plot(epoch_list,train_loss_list)
plt.plot(val_loss_list)
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.title("Training/Validation Loss vs Number of Epochs")
plt.legend(['Train', 'Valid'], loc='upper right')
plt.show()
# Train/Valid Accuracy
plt.plot(epoch_list,train_acc_list)
plt.plot(val_acc_list)
plt.xlabel("Epochs")
plt.ylabel("Training/Validation Accuracy")
plt.title("Accuracy vs Number of Epochs")
plt.legend(['Train', 'Valid'], loc='best')
plt.show()
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
From the above graphs we get some really impressive results **Overall Accuracy**
|
val_acc = sum(val_acc_list[:]).item()/len(val_acc_list)
print("Validation Accuracy of model = {} %".format(val_acc))
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
8. Test our Model Performance
|
# obtain one batch of test images
dataiter = iter(test_loader)
images, labels = dataiter.next()
img = images.numpy()
# move model inputs to cuda, if GPU available
if train_on_gpu:
images = images.cuda()
model.eval() # Required for Evaluation/Test
# get sample outputs
output = model(images)
if type(output) == tuple:
output, _ = output
# convert output probabilities to predicted class
_, preds_tensor = torch.max(output, 1)
preds = np.squeeze(preds_tensor.numpy()) if not train_on_gpu else np.squeeze(preds_tensor.cpu().numpy())
# plot the images in the batch, along with predicted and true labels
fig = plt.figure(figsize=(20, 5))
for idx in np.arange(12):
ax = fig.add_subplot(3, 4, idx+1, xticks=[], yticks=[])
plt.imshow(np.transpose(img[idx], (1, 2, 0)))
ax.set_title("Pr: {} Ac: {}".format(classes[preds[idx]], classes[labels[idx]]),
color=("green" if preds[idx]==labels[idx].item() else "red"))
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
**We can see that the Correctly Classifies Results are Marked in "Green" and the misclassifies ones are "Red"** 8.1 Test our Model Performance with Gabriele Picco's Program **Credits: ** **Gabriele Picco** (https://github.com/GabrielePicco/deep-learning-flower-identifier) **Special Instruction:** 1. **Uncomment the following two code cells while running the notebook.**2. Comment these two blocks while **Commit**, otherwise you will get an error "Too many Output Files" in Kaggle Only.3. If you find a solution to this then let me know.
|
# !git clone https://github.com/GabrielePicco/deep-learning-flower-identifier
# !pip install airtable
# import sys
# sys.path.insert(0, 'deep-learning-flower-identifier')
# from test_model_pytorch_facebook_challenge import calc_accuracy
# calc_accuracy(model, input_image_size=224, use_google_testset=False)
|
_____no_output_____
|
MIT
|
udacity-pytorch-final-lab-guide-part-1.ipynb
|
styluna7/notebooks
|
Exercise: Find correspondences between old and modern english The purpose of this execise is to use two vecsigrafos, one built on UMBC and Wordnet and another one produced by directly running Swivel against a corpus of Shakespeare's complete works, to try to find corelations between old and modern English, e.g. "thou" -> "you", "dost" -> "do", "raiment" -> "clothing". For example, you can try to pick a set of 100 words in "ye olde" English corpus and see how they correlate to UMBC over WordNet.  Next, we prepare the embeddings from the Shakespeare corpus and load a UMBC vecsigrafo, which will provide the two vector spaces to correlate. Download a small text corpus First, we download the corpus into our environment. We will use the Shakespeare's complete works corpus, published as part of Project Gutenberg and pbublicly available.
|
import os
%ls
#!rm -r tutorial
!git clone https://github.com/HybridNLP2018/tutorial
|
fatal: destination path 'tutorial' already exists and is not an empty directory.
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Let us see if the corpus is where we think it is:
|
%cd tutorial/lit
%ls
|
/content/tutorial/lit
[0m[01;34mcoocs[0m/ shakespeare_complete_works.txt [01;34mswivel[0m/ wget-log
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Downloading Swivel
|
!wget http://expertsystemlab.com/hybridNLP18/swivel.zip
!unzip swivel.zip
!rm swivel/*
!rm swivel.zip
|
Redirecting output to โwget-log.1โ.
Archive: swivel.zip
inflating: swivel/analogy.cc
inflating: swivel/distributed.sh
inflating: swivel/eval.mk
inflating: swivel/fastprep.cc
inflating: swivel/fastprep.mk
inflating: swivel/glove_to_shards.py
inflating: swivel/nearest.py
inflating: swivel/prep.py
inflating: swivel/README.md
inflating: swivel/swivel.py
inflating: swivel/text2bin.py
inflating: swivel/vecs.py
inflating: swivel/wordsim.py
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Learn the Swivel embeddings over the Old Shakespeare corpus Calculating the co-occurrence matrix
|
corpus_path = '/content/tutorial/lit/shakespeare_complete_works.txt'
coocs_path = '/content/tutorial/lit/coocs'
shard_size = 512
freq=3
!python /content/tutorial/scripts/swivel/prep.py --input={corpus_path} --output_dir={coocs_path} --shard_size={shard_size} --min_count={freq}
%ls {coocs_path} | head -n 10
|
col_sums.txt
col_vocab.txt
row_sums.txt
row_vocab.txt
shard-000-000.pb
shard-000-001.pb
shard-000-002.pb
shard-000-003.pb
shard-000-004.pb
shard-000-005.pb
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Learning the embeddings from the matrix
|
vec_path = '/content/tutorial/lit/vec/'
!python /content/tutorial/scripts/swivel/swivel.py --input_base_path={coocs_path} \
--output_base_path={vec_path} \
--num_epochs=20 --dim=300 \
--submatrix_rows={shard_size} --submatrix_cols={shard_size}
|
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:187: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/input.py:187: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
WARNING:tensorflow:From /content/tutorial/scripts/swivel/swivel.py:495: Supervisor.__init__ (from tensorflow.python.training.supervisor) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.train.MonitoredTrainingSession
WARNING:tensorflow:Issue encountered when serializing global_step.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Tensor' object has no attribute 'to_proto'
2018-10-08 13:14:16.156023: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-10-08 13:14:16.156566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 0000:00:04.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
2018-10-08 13:14:16.156611: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0
2018-10-08 13:14:18.064223: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-10-08 13:14:18.064387: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] 0
2018-10-08 13:14:18.064482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0: N
2018-10-08 13:14:18.064823: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:42] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2018-10-08 13:14:18.069298: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10759 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7)
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Starting standard services.
INFO:tensorflow:Saving checkpoint to path /content/tutorial/lit/vec/model.ckpt
INFO:tensorflow:Starting queue runners.
INFO:tensorflow:global_step/sec: 0
WARNING:tensorflow:Issue encountered when serializing global_step.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Tensor' object has no attribute 'to_proto'
INFO:tensorflow:Recording summary at step 0.
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INFO:tensorflow:Recording summary at step 7393.
INFO:tensorflow:global_step/sec: 123.316
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INFO:tensorflow:Recording summary at step 14956.
INFO:tensorflow:global_step/sec: 126.049
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INFO:tensorflow:Recording summary at step 22475.
INFO:tensorflow:global_step/sec: 125.318
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INFO:tensorflow:Recording summary at step 30060.
INFO:tensorflow:global_step/sec: 126.417
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INFO:tensorflow:Recording summary at step 37662.
INFO:tensorflow:global_step/sec: 126.7
INFO:tensorflow:local_step=37670 global_step=37670 loss=18.9, 89.0% complete
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WARNING:tensorflow:Issue encountered when serializing global_step.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'Tensor' object has no attribute 'to_proto'
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Checking the context of the 'vec' directory. Should contain checkpoints of the model plus tsv files for column and row embeddings.
|
os.listdir(vec_path)
|
_____no_output_____
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Converting tsv to bin:
|
!python /content/tutorial/scripts/swivel/text2bin.py --vocab={vec_path}vocab.txt --output={vec_path}vecs.bin \
{vec_path}row_embedding.tsv \
{vec_path}col_embedding.tsv
%ls {vec_path}
|
checkpoint
col_embedding.tsv
events.out.tfevents.1539004459.46972dad0a54
graph.pbtxt
model.ckpt-0.data-00000-of-00001
model.ckpt-0.index
model.ckpt-0.meta
model.ckpt-42320.data-00000-of-00001
model.ckpt-42320.index
model.ckpt-42320.meta
row_embedding.tsv
vecs.bin
vocab.txt
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Read stored binary embeddings and inspect them
|
import importlib.util
spec = importlib.util.spec_from_file_location("vecs", "/content/tutorial/scripts/swivel/vecs.py")
m = importlib.util.module_from_spec(spec)
spec.loader.exec_module(m)
shakespeare_vecs = m.Vecs(vec_path + 'vocab.txt', vec_path + 'vecs.bin')
|
Opening vector with expected size 23552 from file /content/tutorial/lit/vec/vocab.txt
vocab size 23552 (unique 23552)
read rows
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Basic method to print the k nearest neighbors for a given word
|
def k_neighbors(vec, word, k=10):
res = vec.neighbors(word)
if not res:
print('%s is not in the vocabulary, try e.g. %s' % (word, vecs.random_word_in_vocab()))
else:
for word, sim in res[:10]:
print('%0.4f: %s' % (sim, word))
k_neighbors(shakespeare_vecs, 'strife')
k_neighbors(shakespeare_vecs,'youth')
|
1.0000: youth
0.3436: tall,
0.3350: vanity,
0.2945: idleness.
0.2929: womb;
0.2847: tall
0.2823: suffering
0.2742: stillness
0.2671: flow'ring
0.2671: observation
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Load vecsigrafo from UMBC over WordNet
|
%ls
!wget https://zenodo.org/record/1446214/files/vecsigrafo_umbc_tlgs_ls_f_6e_160d_row_embedding.tar.gz
%ls
!tar -xvzf vecsigrafo_umbc_tlgs_ls_f_6e_160d_row_embedding.tar.gz
!rm vecsigrafo_umbc_tlgs_ls_f_6e_160d_row_embedding.tar.gz
umbc_wn_vec_path = '/content/tutorial/lit/vecsi_tlgs_wnscd_ls_f_6e_160d/'
|
_____no_output_____
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Extracting the vocabulary from the .tsv file:
|
with open(umbc_wn_vec_path + 'vocab.txt', 'w', encoding='utf_8') as f:
with open(umbc_wn_vec_path + 'row_embedding.tsv', 'r', encoding='utf_8') as vec_lines:
vocab = [line.split('\t')[0].strip() for line in vec_lines]
for word in vocab:
print(word, file=f)
|
_____no_output_____
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
Converting tsv to bin:
|
!python /content/tutorial/scripts/swivel/text2bin.py --vocab={umbc_wn_vec_path}vocab.txt --output={umbc_wn_vec_path}vecs.bin \
{umbc_wn_vec_path}row_embedding.tsv
%ls
umbc_wn_vecs = m.Vecs(umbc_wn_vec_path + 'vocab.txt', umbc_wn_vec_path + 'vecs.bin')
k_neighbors(umbc_wn_vecs, 'lem_California')
|
1.0000: lem_California
0.6301: lem_Central Valley
0.5959: lem_University of California
0.5542: lem_Southern California
0.5254: lem_Santa Cruz
0.5241: lem_Astro Aerospace
0.5168: lem_San Francisco Bay
0.5092: lem_San Diego County
0.5074: lem_Santa Barbara
0.5069: lem_Santa Rosa
|
MIT
|
06_shakespeare_exercise.ipynb
|
flaviomerenda/tutorial
|
T81-558: Applications of Deep Neural Networks**Module 4: Training for Tabular Data*** Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/). Module 4 Material* Part 4.1: Encoding a Feature Vector for Keras Deep Learning [[Video]](https://www.youtube.com/watch?v=Vxz-gfs9nMQ&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_1_feature_encode.ipynb)* Part 4.2: Keras Multiclass Classification for Deep Neural Networks with ROC and AUC [[Video]](https://www.youtube.com/watch?v=-f3bg9dLMks&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_2_multi_class.ipynb)* **Part 4.3: Keras Regression for Deep Neural Networks with RMSE** [[Video]](https://www.youtube.com/watch?v=wNhBUC6X5-E&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_3_regression.ipynb)* Part 4.4: Backpropagation, Nesterov Momentum, and ADAM Neural Network Training [[Video]](https://www.youtube.com/watch?v=VbDg8aBgpck&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_4_backprop.ipynb)* Part 4.5: Neural Network RMSE and Log Loss Error Calculation from Scratch [[Video]](https://www.youtube.com/watch?v=wmQX1t2PHJc&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_04_5_rmse_logloss.ipynb) Google CoLab InstructionsThe following code ensures that Google CoLab is running the correct version of TensorFlow.
|
try:
%tensorflow_version 2.x
COLAB = True
print("Note: using Google CoLab")
except:
print("Note: not using Google CoLab")
COLAB = False
|
Note: not using Google CoLab
|
Apache-2.0
|
t81_558_class_04_3_regression.ipynb
|
akramsystems/t81_558_deep_learning
|
Part 4.3: Keras Regression for Deep Neural Networks with RMSERegression results are evaluated differently than classification. Consider the following code that trains a neural network for regression on the data set **jh-simple-dataset.csv**.
|
import pandas as pd
from scipy.stats import zscore
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
# Read the data set
df = pd.read_csv(
"https://data.heatonresearch.com/data/t81-558/jh-simple-dataset.csv",
na_values=['NA','?'])
# Generate dummies for job
df = pd.concat([df,pd.get_dummies(df['job'],prefix="job")],axis=1)
df.drop('job', axis=1, inplace=True)
# Generate dummies for area
df = pd.concat([df,pd.get_dummies(df['area'],prefix="area")],axis=1)
df.drop('area', axis=1, inplace=True)
# Generate dummies for product
df = pd.concat([df,pd.get_dummies(df['product'],prefix="product")],axis=1)
df.drop('product', axis=1, inplace=True)
# Missing values for income
med = df['income'].median()
df['income'] = df['income'].fillna(med)
# Standardize ranges
df['income'] = zscore(df['income'])
df['aspect'] = zscore(df['aspect'])
df['save_rate'] = zscore(df['save_rate'])
df['subscriptions'] = zscore(df['subscriptions'])
# Convert to numpy - Classification
x_columns = df.columns.drop('age').drop('id')
x = df[x_columns].values
y = df['age'].values
# Create train/test
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.25, random_state=42)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.callbacks import EarlyStopping
# Build the neural network
model = Sequential()
model.add(Dense(25, input_dim=x.shape[1], activation='relu')) # Hidden 1
model.add(Dense(10, activation='relu')) # Hidden 2
model.add(Dense(1)) # Output
model.compile(loss='mean_squared_error', optimizer='adam')
monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3,
patience=5, verbose=1, mode='auto', restore_best_weights=True)
model.fit(x_train,y_train,validation_data=(x_test,y_test),callbacks=[monitor],verbose=2,epochs=1000)
|
Train on 1500 samples, validate on 500 samples
Epoch 1/1000
1500/1500 - 1s - loss: 1905.4454 - val_loss: 1628.1341
Epoch 2/1000
1500/1500 - 0s - loss: 1331.4213 - val_loss: 889.0575
Epoch 3/1000
1500/1500 - 0s - loss: 554.8426 - val_loss: 303.7261
Epoch 4/1000
1500/1500 - 0s - loss: 276.2087 - val_loss: 241.2495
Epoch 5/1000
1500/1500 - 0s - loss: 232.2832 - val_loss: 208.2143
Epoch 6/1000
1500/1500 - 0s - loss: 198.5331 - val_loss: 179.5262
Epoch 7/1000
1500/1500 - 0s - loss: 169.0791 - val_loss: 154.5270
Epoch 8/1000
1500/1500 - 0s - loss: 144.1286 - val_loss: 132.8691
Epoch 9/1000
1500/1500 - 0s - loss: 122.9873 - val_loss: 115.0928
Epoch 10/1000
1500/1500 - 0s - loss: 104.7249 - val_loss: 98.7375
Epoch 11/1000
1500/1500 - 0s - loss: 89.8292 - val_loss: 86.2749
Epoch 12/1000
1500/1500 - 0s - loss: 77.3071 - val_loss: 75.0022
Epoch 13/1000
1500/1500 - 0s - loss: 67.0604 - val_loss: 66.1396
Epoch 14/1000
1500/1500 - 0s - loss: 58.9584 - val_loss: 58.4367
Epoch 15/1000
1500/1500 - 0s - loss: 51.2491 - val_loss: 52.7136
Epoch 16/1000
1500/1500 - 0s - loss: 45.1765 - val_loss: 46.5179
Epoch 17/1000
1500/1500 - 0s - loss: 39.8843 - val_loss: 41.3721
Epoch 18/1000
1500/1500 - 0s - loss: 35.1468 - val_loss: 37.2132
Epoch 19/1000
1500/1500 - 0s - loss: 31.1755 - val_loss: 33.0697
Epoch 20/1000
1500/1500 - 0s - loss: 27.6307 - val_loss: 30.3131
Epoch 21/1000
1500/1500 - 0s - loss: 24.8457 - val_loss: 26.9474
Epoch 22/1000
1500/1500 - 0s - loss: 22.4056 - val_loss: 24.3656
Epoch 23/1000
1500/1500 - 0s - loss: 20.3071 - val_loss: 22.1642
Epoch 24/1000
1500/1500 - 0s - loss: 18.5446 - val_loss: 20.4782
Epoch 25/1000
1500/1500 - 0s - loss: 17.1571 - val_loss: 18.8670
Epoch 26/1000
1500/1500 - 0s - loss: 15.9407 - val_loss: 17.6862
Epoch 27/1000
1500/1500 - 0s - loss: 14.9866 - val_loss: 16.5275
Epoch 28/1000
1500/1500 - 0s - loss: 14.1251 - val_loss: 15.6342
Epoch 29/1000
1500/1500 - 0s - loss: 13.4655 - val_loss: 14.8625
Epoch 30/1000
1500/1500 - 0s - loss: 12.8994 - val_loss: 14.2826
Epoch 31/1000
1500/1500 - 0s - loss: 12.5566 - val_loss: 13.6121
Epoch 32/1000
1500/1500 - 0s - loss: 12.0077 - val_loss: 13.3087
Epoch 33/1000
1500/1500 - 0s - loss: 11.5357 - val_loss: 12.6593
Epoch 34/1000
1500/1500 - 0s - loss: 11.2365 - val_loss: 12.1849
Epoch 35/1000
1500/1500 - 0s - loss: 10.8074 - val_loss: 11.9388
Epoch 36/1000
1500/1500 - 0s - loss: 10.5593 - val_loss: 11.4006
Epoch 37/1000
1500/1500 - 0s - loss: 10.2093 - val_loss: 10.9751
Epoch 38/1000
1500/1500 - 0s - loss: 9.8386 - val_loss: 10.8651
Epoch 39/1000
1500/1500 - 0s - loss: 9.5938 - val_loss: 10.5728
Epoch 40/1000
1500/1500 - 0s - loss: 9.1488 - val_loss: 9.8661
Epoch 41/1000
1500/1500 - 0s - loss: 8.8920 - val_loss: 9.5228
Epoch 42/1000
1500/1500 - 0s - loss: 8.5156 - val_loss: 9.1506
Epoch 43/1000
1500/1500 - 0s - loss: 8.2628 - val_loss: 8.9486
Epoch 44/1000
1500/1500 - 0s - loss: 7.9219 - val_loss: 8.5034
Epoch 45/1000
1500/1500 - 0s - loss: 7.7077 - val_loss: 8.0760
Epoch 46/1000
1500/1500 - 0s - loss: 7.3165 - val_loss: 7.6620
Epoch 47/1000
1500/1500 - 0s - loss: 7.0259 - val_loss: 7.4933
Epoch 48/1000
1500/1500 - 0s - loss: 6.7422 - val_loss: 7.0583
Epoch 49/1000
1500/1500 - 0s - loss: 6.5163 - val_loss: 6.8024
Epoch 50/1000
1500/1500 - 0s - loss: 6.2633 - val_loss: 7.3045
Epoch 51/1000
1500/1500 - 0s - loss: 6.0029 - val_loss: 6.2712
Epoch 52/1000
1500/1500 - 0s - loss: 5.6791 - val_loss: 5.9342
Epoch 53/1000
1500/1500 - 0s - loss: 5.4798 - val_loss: 6.0110
Epoch 54/1000
1500/1500 - 0s - loss: 5.2115 - val_loss: 5.3928
Epoch 55/1000
1500/1500 - 0s - loss: 4.9592 - val_loss: 5.2215
Epoch 56/1000
1500/1500 - 0s - loss: 4.7189 - val_loss: 5.0103
Epoch 57/1000
1500/1500 - 0s - loss: 4.4683 - val_loss: 4.7098
Epoch 58/1000
1500/1500 - 0s - loss: 4.2650 - val_loss: 4.5259
Epoch 59/1000
1500/1500 - 0s - loss: 4.0953 - val_loss: 4.4263
Epoch 60/1000
1500/1500 - 0s - loss: 3.8027 - val_loss: 4.1103
Epoch 61/1000
1500/1500 - 0s - loss: 3.5759 - val_loss: 3.7770
Epoch 62/1000
1500/1500 - 0s - loss: 3.3755 - val_loss: 3.5737
Epoch 63/1000
1500/1500 - 0s - loss: 3.1781 - val_loss: 3.4833
Epoch 64/1000
1500/1500 - 0s - loss: 3.0001 - val_loss: 3.2246
Epoch 65/1000
1500/1500 - 0s - loss: 2.7691 - val_loss: 3.1021
Epoch 66/1000
1500/1500 - 0s - loss: 2.6227 - val_loss: 2.8215
Epoch 67/1000
1500/1500 - 0s - loss: 2.4682 - val_loss: 2.7528
Epoch 68/1000
1500/1500 - 0s - loss: 2.3243 - val_loss: 2.5394
Epoch 69/1000
1500/1500 - 0s - loss: 2.1664 - val_loss: 2.3886
Epoch 70/1000
1500/1500 - 0s - loss: 2.0377 - val_loss: 2.2536
Epoch 71/1000
1500/1500 - 0s - loss: 1.8845 - val_loss: 2.2354
Epoch 72/1000
1500/1500 - 0s - loss: 1.7931 - val_loss: 2.0831
Epoch 73/1000
1500/1500 - 0s - loss: 1.6889 - val_loss: 1.8866
Epoch 74/1000
1500/1500 - 0s - loss: 1.5820 - val_loss: 1.7964
Epoch 75/1000
1500/1500 - 0s - loss: 1.5085 - val_loss: 1.7138
Epoch 76/1000
1500/1500 - 0s - loss: 1.4159 - val_loss: 1.6468
Epoch 77/1000
1500/1500 - 0s - loss: 1.3606 - val_loss: 1.5906
Epoch 78/1000
1500/1500 - 0s - loss: 1.2652 - val_loss: 1.5063
Epoch 79/1000
1500/1500 - 0s - loss: 1.1937 - val_loss: 1.4506
Epoch 80/1000
1500/1500 - 0s - loss: 1.1180 - val_loss: 1.4817
Epoch 81/1000
1500/1500 - 0s - loss: 1.1412 - val_loss: 1.2800
Epoch 82/1000
1500/1500 - 0s - loss: 1.0385 - val_loss: 1.2412
Epoch 83/1000
1500/1500 - 0s - loss: 0.9846 - val_loss: 1.1891
Epoch 84/1000
1500/1500 - 0s - loss: 0.9937 - val_loss: 1.1322
Epoch 85/1000
1500/1500 - 0s - loss: 0.8915 - val_loss: 1.0847
Epoch 86/1000
1500/1500 - 0s - loss: 0.8562 - val_loss: 1.1110
Epoch 87/1000
1500/1500 - 0s - loss: 0.8468 - val_loss: 1.0686
Epoch 88/1000
1500/1500 - 0s - loss: 0.7947 - val_loss: 0.9805
Epoch 89/1000
1500/1500 - 0s - loss: 0.7807 - val_loss: 0.9463
Epoch 90/1000
1500/1500 - 0s - loss: 0.7502 - val_loss: 0.9965
Epoch 91/1000
1500/1500 - 0s - loss: 0.7529 - val_loss: 0.9532
Epoch 92/1000
1500/1500 - 0s - loss: 0.6857 - val_loss: 0.8712
Epoch 93/1000
1500/1500 - 0s - loss: 0.6717 - val_loss: 0.8498
Epoch 94/1000
1500/1500 - 0s - loss: 0.6869 - val_loss: 0.8518
Epoch 95/1000
1500/1500 - 0s - loss: 0.6626 - val_loss: 0.8275
Epoch 96/1000
1500/1500 - 0s - loss: 0.6308 - val_loss: 0.7850
Epoch 97/1000
1500/1500 - 0s - loss: 0.6056 - val_loss: 0.7708
Epoch 98/1000
1500/1500 - 0s - loss: 0.5991 - val_loss: 0.7643
Epoch 99/1000
1500/1500 - 0s - loss: 0.6102 - val_loss: 0.8104
Epoch 100/1000
1500/1500 - 0s - loss: 0.5647 - val_loss: 0.7227
Epoch 101/1000
1500/1500 - 0s - loss: 0.5474 - val_loss: 0.7107
Epoch 102/1000
1500/1500 - 0s - loss: 0.5395 - val_loss: 0.6847
Epoch 103/1000
1500/1500 - 0s - loss: 0.5350 - val_loss: 0.7383
Epoch 104/1000
1500/1500 - 0s - loss: 0.5551 - val_loss: 0.6698
Epoch 105/1000
1500/1500 - 0s - loss: 0.5032 - val_loss: 0.6520
Epoch 106/1000
1500/1500 - 0s - loss: 0.5418 - val_loss: 0.7518
Epoch 107/1000
1500/1500 - 0s - loss: 0.4949 - val_loss: 0.6307
Epoch 108/1000
1500/1500 - 0s - loss: 0.5166 - val_loss: 0.6741
Epoch 109/1000
1500/1500 - 0s - loss: 0.4992 - val_loss: 0.6195
Epoch 110/1000
1500/1500 - 0s - loss: 0.4610 - val_loss: 0.6268
Epoch 111/1000
1500/1500 - 0s - loss: 0.4554 - val_loss: 0.5956
Epoch 112/1000
1500/1500 - 0s - loss: 0.4704 - val_loss: 0.5977
Epoch 113/1000
1500/1500 - 0s - loss: 0.4687 - val_loss: 0.5736
Epoch 114/1000
1500/1500 - 0s - loss: 0.4497 - val_loss: 0.5817
Epoch 115/1000
1500/1500 - 0s - loss: 0.4326 - val_loss: 0.5833
Epoch 116/1000
1500/1500 - 0s - loss: 0.4181 - val_loss: 0.5738
Epoch 117/1000
1500/1500 - 0s - loss: 0.4252 - val_loss: 0.5688
Epoch 118/1000
1500/1500 - 0s - loss: 0.4675 - val_loss: 0.5680
Epoch 119/1000
1500/1500 - 0s - loss: 0.4328 - val_loss: 0.5463
Epoch 120/1000
1500/1500 - 0s - loss: 0.4091 - val_loss: 0.5912
Epoch 121/1000
1500/1500 - 0s - loss: 0.4047 - val_loss: 0.5459
Epoch 122/1000
1500/1500 - 0s - loss: 0.4456 - val_loss: 0.5509
Epoch 123/1000
1500/1500 - 0s - loss: 0.4081 - val_loss: 0.5540
Epoch 124/1000
Restoring model weights from the end of the best epoch.
1500/1500 - 0s - loss: 0.4353 - val_loss: 0.5538
Epoch 00124: early stopping
|
Apache-2.0
|
t81_558_class_04_3_regression.ipynb
|
akramsystems/t81_558_deep_learning
|
Mean Square ErrorThe mean square error is the sum of the squared differences between the prediction ($\hat{y}$) and the expected ($y$). MSE values are not of a particular unit. If an MSE value has decreased for a model, that is good. However, beyond this, there is not much more you can determine. Low MSE values are desired.$ \mbox{MSE} = \frac{1}{n} \sum_{i=1}^n \left(\hat{y}_i - y_i\right)^2 $
|
from sklearn import metrics
# Predict
pred = model.predict(x_test)
# Measure MSE error.
score = metrics.mean_squared_error(pred,y_test)
print("Final score (MSE): {}".format(score))
|
Final score (MSE): 0.5463447829677607
|
Apache-2.0
|
t81_558_class_04_3_regression.ipynb
|
akramsystems/t81_558_deep_learning
|
Root Mean Square ErrorThe root mean square (RMSE) is essentially the square root of the MSE. Because of this, the RMSE error is in the same units as the training data outcome. Low RMSE values are desired.$ \mbox{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^n \left(\hat{y}_i - y_i\right)^2} $
|
import numpy as np
# Measure RMSE error. RMSE is common for regression.
score = np.sqrt(metrics.mean_squared_error(pred,y_test))
print("Final score (RMSE): {}".format(score))
|
Final score (RMSE): 0.7391513938076291
|
Apache-2.0
|
t81_558_class_04_3_regression.ipynb
|
akramsystems/t81_558_deep_learning
|
Lift ChartTo generate a lift chart, perform the following activities:* Sort the data by expected output. Plot the blue line above.* For every point on the x-axis plot the predicted value for that same data point. This is the green line above.* The x-axis is just 0 to 100% of the dataset. The expected always starts low and ends high.* The y-axis is ranged according to the values predicted.Reading a lift chart:* The expected and predict lines should be close. Notice where one is above the ot other.* The below chart is the most accurate on lower age.
|
# Regression chart.
def chart_regression(pred, y, sort=True):
t = pd.DataFrame({'pred': pred, 'y': y.flatten()})
if sort:
t.sort_values(by=['y'], inplace=True)
plt.plot(t['y'].tolist(), label='expected')
plt.plot(t['pred'].tolist(), label='prediction')
plt.ylabel('output')
plt.legend()
plt.show()
# Plot the chart
chart_regression(pred.flatten(),y_test)
|
_____no_output_____
|
Apache-2.0
|
t81_558_class_04_3_regression.ipynb
|
akramsystems/t81_558_deep_learning
|
Test zplot
|
zplot()
zplot(area=0.80, two_tailed=False)
zplot(area=0.80, two_tailed=False, align_right=True)
|
_____no_output_____
|
MIT
|
notebooks/test_plot.ipynb
|
rajvpatil5/ab-framework
|
Test abplot
|
abplot(n=4000, bcr=0.11, d_hat=0.03, show_alpha=True)
|
_____no_output_____
|
MIT
|
notebooks/test_plot.ipynb
|
rajvpatil5/ab-framework
|
About this NotebookIn this notebook, we provide the tensor factorization implementation using an iterative Alternating Least Square (ALS), which is a good starting point for understanding tensor factorization.
|
import numpy as np
from numpy.linalg import inv as inv
|
_____no_output_____
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
Part 1: Matrix Computation Concepts 1) Kronecker product- **Definition**:Given two matrices $A\in\mathbb{R}^{m_1\times n_1}$ and $B\in\mathbb{R}^{m_2\times n_2}$, then, the **Kronecker product** between these two matrices is defined as$$A\otimes B=\left[ \begin{array}{cccc} a_{11}B & a_{12}B & \cdots & a_{1m_2}B \\ a_{21}B & a_{22}B & \cdots & a_{2m_2}B \\ \vdots & \vdots & \ddots & \vdots \\ a_{m_11}B & a_{m_12}B & \cdots & a_{m_1m_2}B \\ \end{array} \right]$$where the symbol $\otimes$ denotes Kronecker product, and the size of resulted $A\otimes B$ is $(m_1m_2)\times (n_1n_2)$ (i.e., $m_1\times m_2$ columns and $n_1\times n_2$ rows).- **Example**:If $A=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ \end{array} \right]$ and $B=\left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10 \\ \end{array} \right]$, then, we have$$A\otimes B=\left[ \begin{array}{cc} 1\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] & 2\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] \\ 3\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] & 4\times \left[ \begin{array}{ccc} 5 & 6 & 7\\ 8 & 9 & 10\\ \end{array} \right] \\ \end{array} \right]$$$$=\left[ \begin{array}{cccccc} 5 & 6 & 7 & 10 & 12 & 14 \\ 8 & 9 & 10 & 16 & 18 & 20 \\ 15 & 18 & 21 & 20 & 24 & 28 \\ 24 & 27 & 30 & 32 & 36 & 40 \\ \end{array} \right]\in\mathbb{R}^{4\times 6}.$$ 2) Khatri-Rao product (`kr_prod`)- **Definition**:Given two matrices $A=\left( \boldsymbol{a}_1,\boldsymbol{a}_2,...,\boldsymbol{a}_r \right)\in\mathbb{R}^{m\times r}$ and $B=\left( \boldsymbol{b}_1,\boldsymbol{b}_2,...,\boldsymbol{b}_r \right)\in\mathbb{R}^{n\times r}$ with same number of columns, then, the **Khatri-Rao product** (or **column-wise Kronecker product**) between $A$ and $B$ is given as follows,$$A\odot B=\left( \boldsymbol{a}_1\otimes \boldsymbol{b}_1,\boldsymbol{a}_2\otimes \boldsymbol{b}_2,...,\boldsymbol{a}_r\otimes \boldsymbol{b}_r \right)\in\mathbb{R}^{(mn)\times r},$$where the symbol $\odot$ denotes Khatri-Rao product, and $\otimes$ denotes Kronecker product.- **Example**:If $A=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ \end{array} \right]=\left( \boldsymbol{a}_1,\boldsymbol{a}_2 \right) $ and $B=\left[ \begin{array}{cc} 5 & 6 \\ 7 & 8 \\ 9 & 10 \\ \end{array} \right]=\left( \boldsymbol{b}_1,\boldsymbol{b}_2 \right) $, then, we have$$A\odot B=\left( \boldsymbol{a}_1\otimes \boldsymbol{b}_1,\boldsymbol{a}_2\otimes \boldsymbol{b}_2 \right) $$$$=\left[ \begin{array}{cc} \left[ \begin{array}{c} 1 \\ 3 \\ \end{array} \right]\otimes \left[ \begin{array}{c} 5 \\ 7 \\ 9 \\ \end{array} \right] & \left[ \begin{array}{c} 2 \\ 4 \\ \end{array} \right]\otimes \left[ \begin{array}{c} 6 \\ 8 \\ 10 \\ \end{array} \right] \\ \end{array} \right]$$$$=\left[ \begin{array}{cc} 5 & 12 \\ 7 & 16 \\ 9 & 20 \\ 15 & 24 \\ 21 & 32 \\ 27 & 40 \\ \end{array} \right]\in\mathbb{R}^{6\times 2}.$$
|
def kr_prod(a, b):
return np.einsum('ir, jr -> ijr', a, b).reshape(a.shape[0] * b.shape[0], -1)
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8], [9, 10]])
print(kr_prod(A, B))
|
[[ 5 12]
[ 7 16]
[ 9 20]
[15 24]
[21 32]
[27 40]]
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
3) CP decomposition CP Combination (`cp_combination`)- **Definition**:The CP decomposition factorizes a tensor into a sum of outer products of vectors. For example, for a third-order tensor $\mathcal{Y}\in\mathbb{R}^{m\times n\times f}$, the CP decomposition can be written as$$\hat{\mathcal{Y}}=\sum_{s=1}^{r}\boldsymbol{u}_{s}\circ\boldsymbol{v}_{s}\circ\boldsymbol{x}_{s},$$or element-wise,$$\hat{y}_{ijt}=\sum_{s=1}^{r}u_{is}v_{js}x_{ts},\forall (i,j,t),$$where vectors $\boldsymbol{u}_{s}\in\mathbb{R}^{m},\boldsymbol{v}_{s}\in\mathbb{R}^{n},\boldsymbol{x}_{s}\in\mathbb{R}^{f}$ are columns of factor matrices $U\in\mathbb{R}^{m\times r},V\in\mathbb{R}^{n\times r},X\in\mathbb{R}^{f\times r}$, respectively. The symbol $\circ$ denotes vector outer product.- **Example**:Given matrices $U=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ \end{array} \right]\in\mathbb{R}^{2\times 2}$, $V=\left[ \begin{array}{cc} 1 & 2 \\ 3 & 4 \\ 5 & 6 \\ \end{array} \right]\in\mathbb{R}^{3\times 2}$ and $X=\left[ \begin{array}{cc} 1 & 5 \\ 2 & 6 \\ 3 & 7 \\ 4 & 8 \\ \end{array} \right]\in\mathbb{R}^{4\times 2}$, then if $\hat{\mathcal{Y}}=\sum_{s=1}^{r}\boldsymbol{u}_{s}\circ\boldsymbol{v}_{s}\circ\boldsymbol{x}_{s}$, then, we have$$\hat{Y}_1=\hat{\mathcal{Y}}(:,:,1)=\left[ \begin{array}{ccc} 31 & 42 & 65 \\ 63 & 86 & 135 \\ \end{array} \right],$$$$\hat{Y}_2=\hat{\mathcal{Y}}(:,:,2)=\left[ \begin{array}{ccc} 38 & 52 & 82 \\ 78 & 108 & 174 \\ \end{array} \right],$$$$\hat{Y}_3=\hat{\mathcal{Y}}(:,:,3)=\left[ \begin{array}{ccc} 45 & 62 & 99 \\ 93 & 130 & 213 \\ \end{array} \right],$$$$\hat{Y}_4=\hat{\mathcal{Y}}(:,:,4)=\left[ \begin{array}{ccc} 52 & 72 & 116 \\ 108 & 152 & 252 \\ \end{array} \right].$$
|
def cp_combine(U, V, X):
return np.einsum('is, js, ts -> ijt', U, V, X)
U = np.array([[1, 2], [3, 4]])
V = np.array([[1, 3], [2, 4], [5, 6]])
X = np.array([[1, 5], [2, 6], [3, 7], [4, 8]])
print(cp_combine(U, V, X))
print()
print('tensor size:')
print(cp_combine(U, V, X).shape)
|
[[[ 31 38 45 52]
[ 42 52 62 72]
[ 65 82 99 116]]
[[ 63 78 93 108]
[ 86 108 130 152]
[135 174 213 252]]]
tensor size:
(2, 3, 4)
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
4) Tensor Unfolding (`ten2mat`)Using numpy reshape to perform 3rd rank tensor unfold operation. [[**link**](https://stackoverflow.com/questions/49970141/using-numpy-reshape-to-perform-3rd-rank-tensor-unfold-operation)]
|
def ten2mat(tensor, mode):
return np.reshape(np.moveaxis(tensor, mode, 0), (tensor.shape[mode], -1), order = 'F')
X = np.array([[[1, 2, 3, 4], [3, 4, 5, 6]],
[[5, 6, 7, 8], [7, 8, 9, 10]],
[[9, 10, 11, 12], [11, 12, 13, 14]]])
print('tensor size:')
print(X.shape)
print('original tensor:')
print(X)
print()
print('(1) mode-1 tensor unfolding:')
print(ten2mat(X, 0))
print()
print('(2) mode-2 tensor unfolding:')
print(ten2mat(X, 1))
print()
print('(3) mode-3 tensor unfolding:')
print(ten2mat(X, 2))
|
tensor size:
(3, 2, 4)
original tensor:
[[[ 1 2 3 4]
[ 3 4 5 6]]
[[ 5 6 7 8]
[ 7 8 9 10]]
[[ 9 10 11 12]
[11 12 13 14]]]
(1) mode-1 tensor unfolding:
[[ 1 3 2 4 3 5 4 6]
[ 5 7 6 8 7 9 8 10]
[ 9 11 10 12 11 13 12 14]]
(2) mode-2 tensor unfolding:
[[ 1 5 9 2 6 10 3 7 11 4 8 12]
[ 3 7 11 4 8 12 5 9 13 6 10 14]]
(3) mode-3 tensor unfolding:
[[ 1 5 9 3 7 11]
[ 2 6 10 4 8 12]
[ 3 7 11 5 9 13]
[ 4 8 12 6 10 14]]
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
Part 2: Tensor CP Factorization using ALS (TF-ALS)Regarding CP factorization as a machine learning problem, we could perform a learning task by minimizing the loss function over factor matrices, that is,$$\min _{U, V, X} \sum_{(i, j, t) \in \Omega}\left(y_{i j t}-\sum_{r=1}^{R}u_{ir}v_{jr}x_{tr}\right)^{2}.$$Within this optimization problem, multiplication among three factor matrices (acted as parameters) makes this problem difficult. Alternatively, we apply the ALS algorithm for CP factorization.In particular, the optimization problem for each row $\boldsymbol{u}_{i}\in\mathbb{R}^{R},\forall i\in\left\{1,2,...,M\right\}$ of factor matrix $U\in\mathbb{R}^{M\times R}$ is given by$$\min _{\boldsymbol{u}_{i}} \sum_{j,t:(i, j, t) \in \Omega}\left[y_{i j t}-\boldsymbol{u}_{i}^\top\left(\boldsymbol{x}_{t}\odot\boldsymbol{v}_{j}\right)\right]\left[y_{i j t}-\boldsymbol{u}_{i}^\top\left(\boldsymbol{x}_{t}\odot\boldsymbol{v}_{j}\right)\right]^\top.$$The least square for this optimization is$$u_{i} \Leftarrow\left(\sum_{j, t, i, j, t ) \in \Omega} \left(x_{t} \odot v_{j}\right)\left(x_{t} \odot v_{j}\right)^{\top}\right)^{-1}\left(\sum_{j, t :(i, j, t) \in \Omega} y_{i j t} \left(x_{t} \odot v_{j}\right)\right), \forall i \in\{1,2, \ldots, M\}.$$The alternating least squares for $V\in\mathbb{R}^{N\times R}$ and $X\in\mathbb{R}^{T\times R}$ are$$\boldsymbol{v}_{j}\Leftarrow\left(\sum_{i,t:(i,j,t)\in\Omega}\left(\boldsymbol{x}_{t}\odot\boldsymbol{u}_{i}\right)\left(\boldsymbol{x}_{t}\odot\boldsymbol{u}_{i}\right)^\top\right)^{-1}\left(\sum_{i,t:(i,j,t)\in\Omega}y_{ijt}\left(\boldsymbol{x}_{t}\odot\boldsymbol{u}_{i}\right)\right),\forall j\in\left\{1,2,...,N\right\},$$$$\boldsymbol{x}_{t}\Leftarrow\left(\sum_{i,j:(i,j,t)\in\Omega}\left(\boldsymbol{v}_{j}\odot\boldsymbol{u}_{i}\right)\left(\boldsymbol{v}_{j}\odot\boldsymbol{u}_{i}\right)^\top\right)^{-1}\left(\sum_{i,j:(i,j,t)\in\Omega}y_{ijt}\left(\boldsymbol{v}_{j}\odot\boldsymbol{u}_{i}\right)\right),\forall t\in\left\{1,2,...,T\right\}.$$
|
def CP_ALS(sparse_tensor, rank, maxiter):
dim1, dim2, dim3 = sparse_tensor.shape
dim = np.array([dim1, dim2, dim3])
U = 0.1 * np.random.rand(dim1, rank)
V = 0.1 * np.random.rand(dim2, rank)
X = 0.1 * np.random.rand(dim3, rank)
pos = np.where(sparse_tensor != 0)
binary_tensor = np.zeros((dim1, dim2, dim3))
binary_tensor[pos] = 1
tensor_hat = np.zeros((dim1, dim2, dim3))
for iters in range(maxiter):
for order in range(dim.shape[0]):
if order == 0:
var1 = kr_prod(X, V).T
elif order == 1:
var1 = kr_prod(X, U).T
else:
var1 = kr_prod(V, U).T
var2 = kr_prod(var1, var1)
var3 = np.matmul(var2, ten2mat(binary_tensor, order).T).reshape([rank, rank, dim[order]])
var4 = np.matmul(var1, ten2mat(sparse_tensor, order).T)
for i in range(dim[order]):
var_Lambda = var3[ :, :, i]
inv_var_Lambda = inv((var_Lambda + var_Lambda.T)/2 + 10e-12 * np.eye(rank))
vec = np.matmul(inv_var_Lambda, var4[:, i])
if order == 0:
U[i, :] = vec.copy()
elif order == 1:
V[i, :] = vec.copy()
else:
X[i, :] = vec.copy()
tensor_hat = cp_combine(U, V, X)
mape = np.sum(np.abs(sparse_tensor[pos] - tensor_hat[pos])/sparse_tensor[pos])/sparse_tensor[pos].shape[0]
rmse = np.sqrt(np.sum((sparse_tensor[pos] - tensor_hat[pos]) ** 2)/sparse_tensor[pos].shape[0])
if (iters + 1) % 100 == 0:
print('Iter: {}'.format(iters + 1))
print('Training MAPE: {:.6}'.format(mape))
print('Training RMSE: {:.6}'.format(rmse))
print()
return tensor_hat, U, V, X
|
_____no_output_____
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
Part 3: Data Organization 1) Matrix StructureWe consider a dataset of $m$ discrete time series $\boldsymbol{y}_{i}\in\mathbb{R}^{f},i\in\left\{1,2,...,m\right\}$. The time series may have missing elements. We express spatio-temporal dataset as a matrix $Y\in\mathbb{R}^{m\times f}$ with $m$ rows (e.g., locations) and $f$ columns (e.g., discrete time intervals),$$Y=\left[ \begin{array}{cccc} y_{11} & y_{12} & \cdots & y_{1f} \\ y_{21} & y_{22} & \cdots & y_{2f} \\ \vdots & \vdots & \ddots & \vdots \\ y_{m1} & y_{m2} & \cdots & y_{mf} \\ \end{array} \right]\in\mathbb{R}^{m\times f}.$$ 2) Tensor StructureWe consider a dataset of $m$ discrete time series $\boldsymbol{y}_{i}\in\mathbb{R}^{nf},i\in\left\{1,2,...,m\right\}$. The time series may have missing elements. We partition each time series into intervals of predifined length $f$. We express each partitioned time series as a matrix $Y_{i}$ with $n$ rows (e.g., days) and $f$ columns (e.g., discrete time intervals per day),$$Y_{i}=\left[ \begin{array}{cccc} y_{11} & y_{12} & \cdots & y_{1f} \\ y_{21} & y_{22} & \cdots & y_{2f} \\ \vdots & \vdots & \ddots & \vdots \\ y_{n1} & y_{n2} & \cdots & y_{nf} \\ \end{array} \right]\in\mathbb{R}^{n\times f},i=1,2,...,m,$$therefore, the resulting structure is a tensor $\mathcal{Y}\in\mathbb{R}^{m\times n\times f}$. **How to transform a data set into something we can use for time series imputation?** Part 4: Experiments on Guangzhou Data Set
|
import scipy.io
tensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/tensor.mat')
dense_tensor = tensor['tensor']
random_matrix = scipy.io.loadmat('../datasets/Guangzhou-data-set/random_matrix.mat')
random_matrix = random_matrix['random_matrix']
random_tensor = scipy.io.loadmat('../datasets/Guangzhou-data-set/random_tensor.mat')
random_tensor = random_tensor['random_tensor']
missing_rate = 0.2
# =============================================================================
### Random missing (RM) scenario:
binary_tensor = np.round(random_tensor + 0.5 - missing_rate)
# =============================================================================
# =============================================================================
### Non-random missing (NM) scenario:
# binary_tensor = np.zeros(dense_tensor.shape)
# for i1 in range(dense_tensor.shape[0]):
# for i2 in range(dense_tensor.shape[1]):
# binary_tensor[i1, i2, :] = np.round(random_matrix[i1, i2] + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
|
_____no_output_____
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
**Question**: Given only the partially observed data $\mathcal{Y}\in\mathbb{R}^{m\times n\times f}$, how can we impute the unknown missing values?The main influential factors for such imputation model are:- `rank`.- `maxiter`.
|
import time
start = time.time()
rank = 80
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
|
Iter: 100
Training MAPE: 0.0809251
Training RMSE: 3.47736
Iter: 200
Training MAPE: 0.0805399
Training RMSE: 3.46261
Iter: 300
Training MAPE: 0.0803688
Training RMSE: 3.45631
Iter: 400
Training MAPE: 0.0802661
Training RMSE: 3.45266
Iter: 500
Training MAPE: 0.0801768
Training RMSE: 3.44986
Iter: 600
Training MAPE: 0.0800948
Training RMSE: 3.44755
Iter: 700
Training MAPE: 0.0800266
Training RMSE: 3.4456
Iter: 800
Training MAPE: 0.0799675
Training RMSE: 3.44365
Iter: 900
Training MAPE: 0.07992
Training RMSE: 3.4419
Iter: 1000
Training MAPE: 0.079885
Training RMSE: 3.44058
Final Imputation MAPE: 0.0833307
Final Imputation RMSE: 3.59283
Running time: 2908 seconds
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
**Experiment results** of missing data imputation using TF-ALS:| scenario |`rank`| `maxiter`| mape | rmse ||:----------|-----:|---------:|-----------:|----------:||**20%, RM**| 80 | 1000 | **0.0833** | **3.5928**||**40%, RM**| 80 | 1000 | **0.0837** | **3.6190**||**20%, NM**| 10 | 1000 | **0.1027** | **4.2960**||**40%, NM**| 10 | 1000 | **0.1028** | **4.3274**| Part 5: Experiments on Birmingham Data Set
|
import scipy.io
tensor = scipy.io.loadmat('../datasets/Birmingham-data-set/tensor.mat')
dense_tensor = tensor['tensor']
random_matrix = scipy.io.loadmat('../datasets/Birmingham-data-set/random_matrix.mat')
random_matrix = random_matrix['random_matrix']
random_tensor = scipy.io.loadmat('../datasets/Birmingham-data-set/random_tensor.mat')
random_tensor = random_tensor['random_tensor']
missing_rate = 0.3
# =============================================================================
### Random missing (RM) scenario:
binary_tensor = np.round(random_tensor + 0.5 - missing_rate)
# =============================================================================
# =============================================================================
### Non-random missing (NM) scenario:
# binary_tensor = np.zeros(dense_tensor.shape)
# for i1 in range(dense_tensor.shape[0]):
# for i2 in range(dense_tensor.shape[1]):
# binary_tensor[i1, i2, :] = np.round(random_matrix[i1,i2] + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
import time
start = time.time()
rank = 30
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
|
Iter: 100
Training MAPE: 0.0509401
Training RMSE: 15.3163
Iter: 200
Training MAPE: 0.0498774
Training RMSE: 14.9599
Iter: 300
Training MAPE: 0.0490062
Training RMSE: 14.768
Iter: 400
Training MAPE: 0.0481006
Training RMSE: 14.6343
Iter: 500
Training MAPE: 0.0474233
Training RMSE: 14.5365
Iter: 600
Training MAPE: 0.0470442
Training RMSE: 14.4642
Iter: 700
Training MAPE: 0.0469617
Training RMSE: 14.4082
Iter: 800
Training MAPE: 0.0470459
Training RMSE: 14.3623
Iter: 900
Training MAPE: 0.0472333
Training RMSE: 14.3235
Iter: 1000
Training MAPE: 0.047408
Training RMSE: 14.2898
Final Imputation MAPE: 0.0583358
Final Imputation RMSE: 18.9148
Running time: 38 seconds
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
**Experiment results** of missing data imputation using TF-ALS:| scenario |`rank`| `maxiter`| mape | rmse ||:----------|-----:|---------:|-----------:|-----------:||**10%, RM**| 30 | 1000 | **0.0615** | **18.5005**||**30%, RM**| 30 | 1000 | **0.0583** | **18.9148**||**10%, NM**| 10 | 1000 | **0.1447** | **41.6710**||**30%, NM**| 10 | 1000 | **0.1765** | **63.8465**| Part 6: Experiments on Hangzhou Data Set
|
import scipy.io
tensor = scipy.io.loadmat('../datasets/Hangzhou-data-set/tensor.mat')
dense_tensor = tensor['tensor']
random_matrix = scipy.io.loadmat('../datasets/Hangzhou-data-set/random_matrix.mat')
random_matrix = random_matrix['random_matrix']
random_tensor = scipy.io.loadmat('../datasets/Hangzhou-data-set/random_tensor.mat')
random_tensor = random_tensor['random_tensor']
missing_rate = 0.4
# =============================================================================
### Random missing (RM) scenario:
binary_tensor = np.round(random_tensor + 0.5 - missing_rate)
# =============================================================================
# =============================================================================
### Non-random missing (NM) scenario:
# binary_tensor = np.zeros(dense_tensor.shape)
# for i1 in range(dense_tensor.shape[0]):
# for i2 in range(dense_tensor.shape[1]):
# binary_tensor[i1, i2, :] = np.round(random_matrix[i1, i2] + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
import time
start = time.time()
rank = 50
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
|
Iter: 100
Training MAPE: 0.176548
Training RMSE: 17.0263
Iter: 200
Training MAPE: 0.174888
Training RMSE: 16.8609
Iter: 300
Training MAPE: 0.175056
Training RMSE: 16.7835
Iter: 400
Training MAPE: 0.174988
Training RMSE: 16.7323
Iter: 500
Training MAPE: 0.175013
Training RMSE: 16.6942
Iter: 600
Training MAPE: 0.174928
Training RMSE: 16.6654
Iter: 700
Training MAPE: 0.174722
Training RMSE: 16.6441
Iter: 800
Training MAPE: 0.174565
Training RMSE: 16.6284
Iter: 900
Training MAPE: 0.174454
Training RMSE: 16.6159
Iter: 1000
Training MAPE: 0.174409
Training RMSE: 16.6054
Final Imputation MAPE: 0.209776
Final Imputation RMSE: 100.315
Running time: 279 seconds
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
**Experiment results** of missing data imputation using TF-ALS:| scenario |`rank`| `maxiter`| mape | rmse ||:----------|-----:|---------:|-----------:|----------:||**20%, RM**| 50 | 1000 | **0.1991** |**111.303**||**40%, RM**| 50 | 1000 | **0.2098** |**100.315**||**20%, NM**| 5 | 1000 | **0.2837** |**42.6136**||**40%, NM**| 5 | 1000 | **0.2811** |**38.4201**| Part 7: Experiments on New York Data Set
|
import scipy.io
tensor = scipy.io.loadmat('../datasets/NYC-data-set/tensor.mat')
dense_tensor = tensor['tensor']
rm_tensor = scipy.io.loadmat('../datasets/NYC-data-set/rm_tensor.mat')
rm_tensor = rm_tensor['rm_tensor']
nm_tensor = scipy.io.loadmat('../datasets/NYC-data-set/nm_tensor.mat')
nm_tensor = nm_tensor['nm_tensor']
missing_rate = 0.1
# =============================================================================
### Random missing (RM) scenario
### Set the RM scenario by:
# binary_tensor = np.round(rm_tensor + 0.5 - missing_rate)
# =============================================================================
# =============================================================================
### Non-random missing (NM) scenario
### Set the NM scenario by:
binary_tensor = np.zeros(dense_tensor.shape)
for i1 in range(dense_tensor.shape[0]):
for i2 in range(dense_tensor.shape[1]):
for i3 in range(61):
binary_tensor[i1, i2, i3 * 24 : (i3 + 1) * 24] = np.round(nm_tensor[i1, i2, i3] + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
import time
start = time.time()
rank = 30
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
|
Iter: 100
Training MAPE: 0.511739
Training RMSE: 4.07981
Iter: 200
Training MAPE: 0.501094
Training RMSE: 4.0612
Iter: 300
Training MAPE: 0.504264
Training RMSE: 4.05578
Iter: 400
Training MAPE: 0.507211
Training RMSE: 4.05119
Iter: 500
Training MAPE: 0.509956
Training RMSE: 4.04623
Iter: 600
Training MAPE: 0.51046
Training RMSE: 4.04129
Iter: 700
Training MAPE: 0.509797
Training RMSE: 4.03294
Iter: 800
Training MAPE: 0.509531
Training RMSE: 4.02976
Iter: 900
Training MAPE: 0.509265
Training RMSE: 4.02861
Iter: 1000
Training MAPE: 0.508873
Training RMSE: 4.02796
Final Imputation MAPE: 0.540363
Final Imputation RMSE: 5.66633
Running time: 742 seconds
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
**Experiment results** of missing data imputation using TF-ALS:| scenario |`rank`| `maxiter`| mape | rmse ||:----------|-----:|---------:|-----------:|----------:||**10%, RM**| 30 | 1000 | **0.5262** | **6.2444**||**30%, RM**| 30 | 1000 | **0.5488** | **6.8968**||**10%, NM**| 30 | 1000 | **0.5170** | **5.9863**||**30%, NM**| 30 | 100 | **-** | **-**| Part 8: Experiments on Seattle Data Set
|
import pandas as pd
dense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)
RM_mat = pd.read_csv('../datasets/Seattle-data-set/RM_mat.csv', index_col = 0)
dense_mat = dense_mat.values
RM_mat = RM_mat.values
dense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])
RM_tensor = RM_mat.reshape([RM_mat.shape[0], 28, 288])
missing_rate = 0.2
# =============================================================================
### Random missing (RM) scenario
### Set the RM scenario by:
binary_tensor = np.round(RM_tensor + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
import time
start = time.time()
rank = 50
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
import pandas as pd
dense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)
RM_mat = pd.read_csv('../datasets/Seattle-data-set/RM_mat.csv', index_col = 0)
dense_mat = dense_mat.values
RM_mat = RM_mat.values
dense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])
RM_tensor = RM_mat.reshape([RM_mat.shape[0], 28, 288])
missing_rate = 0.4
# =============================================================================
### Random missing (RM) scenario
### Set the RM scenario by:
binary_tensor = np.round(RM_tensor + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
import time
start = time.time()
rank = 50
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
import pandas as pd
dense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)
NM_mat = pd.read_csv('../datasets/Seattle-data-set/NM_mat.csv', index_col = 0)
dense_mat = dense_mat.values
NM_mat = NM_mat.values
dense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])
missing_rate = 0.2
# =============================================================================
### Non-random missing (NM) scenario
### Set the NM scenario by:
binary_tensor = np.zeros((dense_mat.shape[0], 28, 288))
for i1 in range(binary_tensor.shape[0]):
for i2 in range(binary_tensor.shape[1]):
binary_tensor[i1, i2, :] = np.round(NM_mat[i1, i2] + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
import time
start = time.time()
rank = 10
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
import pandas as pd
dense_mat = pd.read_csv('../datasets/Seattle-data-set/mat.csv', index_col = 0)
NM_mat = pd.read_csv('../datasets/Seattle-data-set/NM_mat.csv', index_col = 0)
dense_mat = dense_mat.values
NM_mat = NM_mat.values
dense_tensor = dense_mat.reshape([dense_mat.shape[0], 28, 288])
missing_rate = 0.4
# =============================================================================
### Non-random missing (NM) scenario
### Set the NM scenario by:
binary_tensor = np.zeros((dense_mat.shape[0], 28, 288))
for i1 in range(binary_tensor.shape[0]):
for i2 in range(binary_tensor.shape[1]):
binary_tensor[i1, i2, :] = np.round(NM_mat[i1, i2] + 0.5 - missing_rate)
# =============================================================================
sparse_tensor = np.multiply(dense_tensor, binary_tensor)
import time
start = time.time()
rank = 10
maxiter = 1000
tensor_hat, U, V, X = CP_ALS(sparse_tensor, rank, maxiter)
pos = np.where((dense_tensor != 0) & (sparse_tensor == 0))
final_mape = np.sum(np.abs(dense_tensor[pos] - tensor_hat[pos])/dense_tensor[pos])/dense_tensor[pos].shape[0]
final_rmse = np.sqrt(np.sum((dense_tensor[pos] - tensor_hat[pos]) ** 2)/dense_tensor[pos].shape[0])
print('Final Imputation MAPE: {:.6}'.format(final_mape))
print('Final Imputation RMSE: {:.6}'.format(final_rmse))
print()
end = time.time()
print('Running time: %d seconds'%(end - start))
|
Iter: 100
Training MAPE: 0.0996282
Training RMSE: 5.55963
Iter: 200
Training MAPE: 0.0992568
Training RMSE: 5.53825
Iter: 300
Training MAPE: 0.0986723
Training RMSE: 5.51806
Iter: 400
Training MAPE: 0.0967838
Training RMSE: 5.46447
Iter: 500
Training MAPE: 0.0962312
Training RMSE: 5.44762
Iter: 600
Training MAPE: 0.0961017
Training RMSE: 5.44322
Iter: 700
Training MAPE: 0.0959531
Training RMSE: 5.43927
Iter: 800
Training MAPE: 0.0958815
Training RMSE: 5.43619
Iter: 900
Training MAPE: 0.0958781
Training RMSE: 5.4344
Iter: 1000
Training MAPE: 0.0958921
Training RMSE: 5.43266
Final Imputation MAPE: 0.10038
Final Imputation RMSE: 5.7034
Running time: 304 seconds
|
MIT
|
experiments/Imputation-TF-ALS.ipynb
|
shawnwang-tech/transdim
|
Let's look at:Number of labels per image (histogram)Quality score per image for images with multiple labels (sigmoid?)
|
import csv
from itertools import islice
from collections import defaultdict
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torchvision
import numpy as np
CSV_PATH = 'wgangp_data.csv'
realness = {}
# real_votes = defaultdict(int)
# fake_votes = defaultdict(int)
total_votes = defaultdict(int)
correct_votes = defaultdict(int)
with open(CSV_PATH) as f:
dictreader = csv.DictReader(f)
for line in dictreader:
img_name = line['img']
assert(line['realness'] in ('True', 'False'))
assert(line['correctness'] in ('True', 'False'))
realness[img_name] = line['realness'] == 'True'
if line['correctness'] == 'True':
correct_votes[img_name] += 1
total_votes[img_name] += 1
pdx = pd.read_csv(CSV_PATH)
pdx
pdx[pdx.groupby('img').count() > 50]
pdx
#df.img
# print(df.columns)
# print(df['img'])
# How much of the time do people guess "fake"? Slightly more than half!
pdx[pdx.correctness != pdx.realness].count()/pdx.count()
# How much of the time do people guess right? 94.4%
pdx[pdx.correctness].count()/pdx.count()
#90.3% of the time, real images are correctly labeled as real
pdx[pdx.realness][pdx.correctness].count()/pdx[pdx.realness].count()
#98.5% of the time, fake images are correctly labeled as fake
pdx[~pdx.realness][pdx.correctness].count()/pdx[~pdx.realness].count()
len(total_votes.values())
img_dict = {img: [realness[img], correct_votes[img], total_votes[img], correct_votes[img]/total_votes[img]] for img in realness }
# print(img_dict.keys())
#img_dict['celeba500/005077_crop.jpg']
plt.hist([v[3] for k,v in img_dict.items() if 'celeb' in k])
def getVotesDict(img_dict):
votes_dict = defaultdict(int)
for img in total_votes:
votes_dict[img_dict[img][2]] += 1
return votes_dict
votes_dict = getVotesDict(img_dict)
for i in sorted(votes_dict.keys()):
print(i, votes_dict[i])
selected_img_dict = {img:value for img, value in img_dict.items() if img_dict[img][2] > 10}
less_than_50_dict = {img:value for img, value in img_dict.items() if img_dict[img][2] < 10}
imgs_over_50 = list(selected_img_dict.keys())
# print(len(selected_img_dict))
# print(imgs_over_50)
pdx_50 = pdx[pdx.img.apply(lambda x: x in imgs_over_50)]
len(pdx_50)
pdx_under_50 = pdx[pdx.img.apply(lambda x: x not in imgs_over_50)]
len(pdx_under_50)
len(pdx_under_50[pdx_under_50.img.apply(lambda x: 'wgan' not in x)])
correctness = sorted([value[3] for key, value in selected_img_dict.items()])
print(correctness)
plt.hist(correctness)
plt.show()
correctness = sorted([value[3] for key, value in less_than_50_dict.items()])
# print(correctness)
plt.hist(correctness)
plt.show()
ct = []
# selected_img = [img in total_votes.keys() if total_votes[img] > 1 ]
discriminator = torch.load('discriminator.pt', map_location='cpu')
# torch.load_state_dict('discriminator.pt')
discriminator(torch.zeros(64,64,3))
|
_____no_output_____
|
MIT
|
wgan_experiment/WGAN_experiment.ipynb
|
kolchinski/humanception-score
|
Naive Bayes ClassifierPredicting positivty/negativity of movie reviews using Naive Bayes algorithm 1. Import DatasetLabels:* 0 : Negative review* 1 : Positive review
|
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
reviews = pd.read_csv('ratings_train.txt', delimiter='\t')
reviews.head(10)
#divide between negative and positive reviews with more than 30 words in length
neg = reviews[(reviews.document.str.len() >= 30) & (reviews.label == 0)].sample(3000, random_state=43)
pos = reviews[(reviews.document.str.len() >= 30) & (reviews.label == 1)].sample(3000, random_state=43)
pos.head()
#NLP method
import re
import konlpy
from konlpy.tag import Twitter
okt = Twitter()
def parse(s):
s = re.sub(r'[?$.!,-_\'\"(){}~]+', '', s)
try:
return okt.nouns(s)
except:
return []
#okt.morphs is another option
neg['parsed_doc'] = neg.document.apply(parse)
pos['parsed_doc'] = pos.document.apply(parse)
neg.head()
pos.head()
# create 5800 training data / 200 test data
neg_train = neg[:2900]
pos_train = pos[:2900]
neg_test = neg[2900:]
pos_test = pos[2900:]
|
_____no_output_____
|
MIT
|
algorithm_exercise/semantic_analysis/semantic_analysis_naive_bayes_algorithm.ipynb
|
jbaeckn/learning_projects
|
2. Create Corpus
|
neg_corpus = set(neg_train.parsed_doc.sum())
pos_corpus = set(pos_train.parsed_doc.sum())
corpus = list((neg_corpus).union(pos_corpus))
print('corpus length : ', len(corpus))
corpus[:10]
|
_____no_output_____
|
MIT
|
algorithm_exercise/semantic_analysis/semantic_analysis_naive_bayes_algorithm.ipynb
|
jbaeckn/learning_projects
|
3. Create Bag of Words
|
from collections import OrderedDict
neg_bow_vecs = []
for _, doc in neg.parsed_doc.items():
bow_vecs = OrderedDict()
for w in corpus:
if w in doc:
bow_vecs[w] = 1
else:
bow_vecs[w] = 0
neg_bow_vecs.append(bow_vecs)
pos_bow_vecs = []
for _, doc in pos.parsed_doc.items():
bow_vecs = OrderedDict()
for w in corpus:
if w in doc:
bow_vecs[w] = 1
else:
bow_vecs[w] = 0
pos_bow_vecs.append(bow_vecs)
#bag of word vector example
#this length is equal to the length of the corpus
neg_bow_vecs[0].values()
|
_____no_output_____
|
MIT
|
algorithm_exercise/semantic_analysis/semantic_analysis_naive_bayes_algorithm.ipynb
|
jbaeckn/learning_projects
|
4. Model Training $n$ is the dimension of each document, in other words, the length of corpus $$\large p(pos|doc) = \Large \frac{p(doc|pos) \cdot p(pos)}{p(doc)}$$$$\large p(neg|doc) = \Large \frac{p(doc|neg) \cdot p(neg)}{p(doc)}$$**Likelihood functions:** $p(word_{i}|pos) = \large \frac{\text{the number of positive documents that contain the word}}{\text{the number of positive documents}}$$p(word_{i}|neg) = \large \frac{\text{the number of negative documents that contain the word}}{\text{the number of negative documents}}$
|
import numpy as np
corpus[:5]
list(neg_train.parsed_doc.items())[0]
#this counts how many times a word in corpus appeares in neg_train data
neg_words_likelihood_cnts = {}
for w in corpus:
cnt = 0
for _, doc in neg_train.parsed_doc.items():
if w in doc:
cnt += 1
neg_words_likelihood_cnts[w] = cnt
#this counts how many times a word in corpus appeares in pos_train data : p(neg)
pos_words_likelihood_cnts = {}
for w in corpus:
cnt = 0
for _, doc in pos_train.parsed_doc.items():
if w in doc:
cnt += 1
pos_words_likelihood_cnts[w] = cnt
import operator
sorted(neg_words_likelihood_cnts.items(), key=operator.itemgetter(1), reverse=True)[:10]
sorted(pos_words_likelihood_cnts.items(), key=operator.itemgetter(1), reverse=True)[:10]
|
_____no_output_____
|
MIT
|
algorithm_exercise/semantic_analysis/semantic_analysis_naive_bayes_algorithm.ipynb
|
jbaeckn/learning_projects
|
5. Classifier* We represent each documents in terms of bag of words. If the size of Corpus is $n$, this means that each bag of word of document is $n-dimensional$* When there isn't a word, we use **Laclacian Smoothing**
|
test_data = pd.concat([neg_test, pos_test], axis=0)
test_data.head()
def predict(doc):
pos_prior, neg_prior = 1/2, 1/2 #because we have equal number of pos and neg training documents
# Posterior of pos
pos_prob = np.log(1)
for word in corpus:
if word in doc:
# the word is in the current document and has appeared in pos documents
if word in pos_words_likelihood_cnts:
pos_prob += np.log((pos_words_likelihood_cnts[word] + 1) / (len(pos_train) + len(corpus)))
else:
# the word is in the current document, but has never appeared in pos documents : Laplacian
pos_prob += np.log(1 / (len(pos_train) + len(corpus)))
else:
# the word is not in the current document, but has appeared in pos documents
# we can find the possibility that the word is not in pos
if word in pos_words_likelihood_cnts:
pos_prob += \
np.log((len(pos_train) - pos_words_likelihood_cnts[word] + 1) / (len(pos_train) + len(corpus)))
else:
# the word is not in the current document, and has never appeared in pos documents : Laplacian
pos_prob += np.log((len(pos_train) + 1) / (len(pos_train) + len(corpus)))
pos_prob += np.log(pos_prior)
# Posterior of neg
neg_prob = 1
for word in corpus:
if word in doc:
# ๋จ์ด๊ฐ ํ์ฌ ๋ฌธ์ฅ์ ์กด์ฌํ๊ณ , neg ๋ฌธ์ฅ์ ๋์จ์ ์ด ์๋ ๊ฒฝ์ฐ
if word in neg_words_likelihood_cnts:
neg_prob += np.log((neg_words_likelihood_cnts[word] + 1) / (len(neg_train) + len(corpus)))
else:
# ๋จ์ด๊ฐ ํ์ฌ ๋ฌธ์ฅ์ ์กด์ฌํ๊ณ , neg ๋ฌธ์ฅ์์ ํ ๋ฒ๋ ๋์จ์ ์ด ์๋ ๊ฒฝ์ฐ : ๋ผํ๋ผ์์ ์ค๋ฌด๋ฉ
neg_prob += np.log(1 / (len(neg_train) + len(corpus)))
else:
# ๋จ์ด๊ฐ ํ์ฌ ๋ฌธ์ฅ์ ์กด์ฌํ์ง ์๊ณ , neg ๋ฌธ์ฅ์ ๋์จ์ ์ด ์๋ ๊ฒฝ์ฐ (neg์์ ํด๋น๋จ์ด๊ฐ ์๋ ํ๋ฅ ์ ๊ตฌํ ์ ์๋ค.)
if word in neg_words_likelihood_cnts:
neg_prob += \
np.log((len(neg_train) - neg_words_likelihood_cnts[word] + 1) / (len(neg_train) + len(corpus)))
else:
# ๋จ์ด๊ฐ ํ์ฌ ๋ฌธ์ฅ์ ์กด์ฌํ์ง ์๊ณ , pos ๋ฌธ์ฅ์์ ๋จ ํ ๋ฒ๋ ๋์จ์ ์ด ์๋ ๊ฒฝ์ฐ : ๋ผํ๋ผ์์ ์ค๋ฌด๋ฉ
neg_prob += np.log((len(neg_train) + 1) / (len(neg_train) + len(corpus)))
neg_prob += np.log(neg_prior)
if pos_prob >= neg_prob:
return 1
else:
return 0
test_data['pred'] = test_data.parsed_doc.apply(predict)
test_data.head()
test_data.shape
sum(test_data.label ^ test_data.pred)
|
_____no_output_____
|
MIT
|
algorithm_exercise/semantic_analysis/semantic_analysis_naive_bayes_algorithm.ipynb
|
jbaeckn/learning_projects
|
There are a total of 200 test documents, and of these 200 tests only 46 were different
|
1 - sum(test_data.label ^ test_data.pred) / len(test_data)
|
_____no_output_____
|
MIT
|
algorithm_exercise/semantic_analysis/semantic_analysis_naive_bayes_algorithm.ipynb
|
jbaeckn/learning_projects
|
Auditing a dataframeIn this notebook, we shall demonstrate how to use `privacypanda` to _audit_ the privacy of your data. `privacypanda` provides a simple function which prints the names of any columns which break privacy. Currently, these are:- Addresses - E.g. "10 Downing Street"; "221b Baker St"; "EC2R 8AH"- Phonenumbers (UK mobile) - E.g. "+447123456789"- Email addresses - Ending in ".com", ".co.uk", ".org", ".edu" (to be expanded soon)
|
%load_ext watermark
%watermark -n -p pandas,privacypanda -g
import pandas as pd
import privacypanda as pp
|
_____no_output_____
|
Apache-2.0
|
examples/01_auditing_a_dataframe.ipynb
|
TTitcombe/PrivacyPanda
|
--- Firstly, we need data
|
data = pd.DataFrame(
{
"user ID": [
1665,
1,
5287,
42,
],
"User email": [
"xxxxxxxxxxxxx",
"xxxxxxxx",
"I'm not giving you that",
"[email protected]",
],
"User address": [
"AB1 1AB",
"",
"XXX XXX",
"EC2R 8AH",
],
"Likes raclette": [
1,
0,
1,
1,
],
}
)
|
_____no_output_____
|
Apache-2.0
|
examples/01_auditing_a_dataframe.ipynb
|
TTitcombe/PrivacyPanda
|
You will notice two things about this dataframe:1. _Some_ of the data has already been anonymized, for example by replacing characters with "x"s. However, the person who collected this data has not been fastidious with its cleaning as there is still some raw, potentially problematic private information. As the dataset grows, it becomes easier to miss entries with private information2. Not all columns expose privacy: "Likes raclette" is pretty benign information (but be careful, lots of benign information can be combined to form a unique fingerprint identifying an individual - let's not worry about this at the moment, though), and "user ID" is already an anonymized labelling of an individual. --- Auditing the data's privacyAs a data scientist, we want a simple way to tell which columns, if any break privacy. More importantly, _how_ they break privacy determines how we deal with them. For example, emails will likely be superfluous information for analysis and can therefore be removed from the data, but age may be important and so we may wish instead to apply differential privacy to the dataset.We can use `privacypanda`'s `report_privacy` function to see which data is problematic.
|
report = pp.report_privacy(data)
print(report)
|
User address: ['address']
User email: ['email']
|
Apache-2.0
|
examples/01_auditing_a_dataframe.ipynb
|
TTitcombe/PrivacyPanda
|
read datafiles- C-18 for language population- C-13 for particular age-range population from a state
|
c18=pd.read_excel('datasets/C-18.xlsx',skiprows=6,header=None,engine='openpyxl')
c13=pd.read_excel('datasets/C-13.xls',skiprows=7,header=None)
|
_____no_output_____
|
MIT
|
Q8_asgn2.ipynb
|
sunil-dhaka/census-language-analysis
|
particular age groups are- 5-9- 10-14- 15-19- 20-24- 25-29- 30-49- 50-69- 70+- Age not stated obtain useful data from C-13 and C-18 for age-groups- first get particular state names for identifying specific states- get particular age-groups from C-18 file- make list of particular age group row/col for a particular states- now just simply iterate through each state to get relevent data and store it into a csv file - to get total pop of particular age-range I have used C-13 file - to get total pop that speaks more than 3 languages from a state in a particular age-range I have used C-18 file
|
# STATE_NAMES=[list(np.unique(c18.iloc[:,2].values))]
STATE_NAMES=[]
for state in c18.iloc[:,2].values:
if not (state in STATE_NAMES):
STATE_NAMES.append(state)
AGE_GROUPS=list(c18.iloc[1:10,4].values)
# although it is a bit of manual work but it is worth the efforts
AGE_GROUP_RANGES=[list(range(5,10)),list(range(10,15)),list(range(15,20)),list(range(20,25)),list(range(25,30)),list(range(30,50)),list(range(50,70)),list(range(70,100))+['100+'],['Age not stated']]
useful_data=[]
for i,state in enumerate(STATE_NAMES):
for j,age_grp in enumerate(AGE_GROUPS):
# this list is to get only the years in the particular age-group
true_false_list=[]
for single_year_age in c13.iloc[:,4].values:
if single_year_age in AGE_GROUP_RANGES[j]:
true_false_list.append(True)
else:
true_false_list.append(False)
# here i is the state code
male_pop=c13[(c13.loc[:,1]==i) & (true_false_list)].iloc[:,6].values.sum()
female_pop=c13[(c13.loc[:,1]==i) & (true_false_list)].iloc[:,7].values.sum()
# tri
tri_male=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,9]
tri_female=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,10]
#bi
bi_male=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,6] - tri_male
bi_female=c18[(c18.iloc[:,0]==i) & (c18.iloc[:,4]==age_grp) & (c18.iloc[:,3]=='Total')].iloc[0,7] - tri_female
#uni
uni_male=male_pop-bi_male-tri_male
uni_female=female_pop-bi_female-tri_female
item={
'state-code':i,
'state-name':state,
'age-group':age_grp,
'age-group-male-pop':male_pop,
'age-group-female-pop':female_pop,
'tri-male-ratio':tri_male/male_pop,
'tri-female-ratio':tri_female/female_pop,
'bi-male-ratio':bi_male/male_pop,
'bi-female-ratio':bi_female/female_pop,
'uni-male-ratio':uni_male/male_pop,
'uni-female-ratio':uni_female/female_pop
}
useful_data.append(item)
df=pd.DataFrame(useful_data)
|
_____no_output_____
|
MIT
|
Q8_asgn2.ipynb
|
sunil-dhaka/census-language-analysis
|
age-analysis - get highest ratio age-group for a state and store it into csv file- above process can be repeated for all parts of the question
|
tri_list=[]
bi_list=[]
uni_list=[]
for i in range(36):
male_values=df[df['state-code']==i].sort_values(by='tri-male-ratio',ascending=False).iloc[0,[2,5]].values
female_values=df[df['state-code']==i].sort_values(by='tri-male-ratio',ascending=False).iloc[0,[2,6]].values
tri_item={
'state/ut':i,
'age-group-males':male_values[0],
'ratio-males':male_values[1],
'age-group-females':female_values[0],
'ratio-females':female_values[1]
}
tri_list.append(tri_item)
male_values=df[df['state-code']==i].sort_values(by='bi-male-ratio',ascending=False).iloc[0,[2,7]].values
female_values=df[df['state-code']==i].sort_values(by='bi-male-ratio',ascending=False).iloc[0,[2,8]].values
bi_item={
'state/ut':i,
'age-group-males':male_values[0],
'ratio-males':male_values[1],
'age-group-females':female_values[0],
'ratio-females':female_values[1]
}
bi_list.append(bi_item)
male_values=df[df['state-code']==i].sort_values(by='uni-male-ratio',ascending=False).iloc[0,[2,9]].values
female_values=df[df['state-code']==i].sort_values(by='uni-male-ratio',ascending=False).iloc[0,[2,10]].values
uni_item={
'state/ut':i,
'age-group-males':male_values[0],
'ratio-males':male_values[1],
'age-group-females':female_values[0],
'ratio-females':female_values[1]
}
uni_list.append(uni_item)
|
_____no_output_____
|
MIT
|
Q8_asgn2.ipynb
|
sunil-dhaka/census-language-analysis
|
- convert into pandas dataframes and store into CSVs
|
tri_df=pd.DataFrame(tri_list)
bi_df=pd.DataFrame(bi_list)
uni_df=pd.DataFrame(uni_list)
tri_df.to_csv('outputs/age-gender-a.csv',index=False)
bi_df.to_csv('outputs/age-gender-b.csv',index=False)
uni_df.to_csv('outputs/age-gender-c.csv',index=False)
|
_____no_output_____
|
MIT
|
Q8_asgn2.ipynb
|
sunil-dhaka/census-language-analysis
|
observations- in almost all states(and all cases) both highest ratio female and male age-groups are same.- interestingly in only one language case for all states '5-9' age group dominates, and it is also quite intutive; since at that early stage in life children only speak their mother toung only
|
uni_df
|
_____no_output_____
|
MIT
|
Q8_asgn2.ipynb
|
sunil-dhaka/census-language-analysis
|
Copyright 2018 The TensorFlow Authors.Licensed under the Apache License, Version 2.0 (the "License");
|
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://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
|
site/en/guide/data.ipynb
|
zyberg2091/docs
|
tf.data: Build TensorFlow input pipelines View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook The `tf.data` API enables you to build complex input pipelines from simple,reusable pieces. For example, the pipeline for an image model might aggregatedata from files in a distributed file system, apply random perturbations to eachimage, and merge randomly selected images into a batch for training. Thepipeline for a text model might involve extracting symbols from raw text data,converting them to embedding identifiers with a lookup table, and batchingtogether sequences of different lengths. The `tf.data` API makes it possible tohandle large amounts of data, read from different data formats, and performcomplex transformations.The `tf.data` API introduces a `tf.data.Dataset` abstraction that represents asequence of elements, in which each element consists of one or more components.For example, in an image pipeline, an element might be a single trainingexample, with a pair of tensor components representing the image and its label.There are two distinct ways to create a dataset:* A data **source** constructs a `Dataset` from data stored in memory or in one or more files.* A data **transformation** constructs a dataset from one or more `tf.data.Dataset` objects.
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import tensorflow as tf
import pathlib
import os
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
np.set_printoptions(precision=4)
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Apache-2.0
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site/en/guide/data.ipynb
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zyberg2091/docs
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Basic mechanicsTo create an input pipeline, you must start with a data *source*. For example,to construct a `Dataset` from data in memory, you can use`tf.data.Dataset.from_tensors()` or `tf.data.Dataset.from_tensor_slices()`.Alternatively, if your input data is stored in a file in the recommendedTFRecord format, you can use `tf.data.TFRecordDataset()`.Once you have a `Dataset` object, you can *transform* it into a new `Dataset` bychaining method calls on the `tf.data.Dataset` object. For example, you canapply per-element transformations such as `Dataset.map()`, and multi-elementtransformations such as `Dataset.batch()`. See the documentation for`tf.data.Dataset` for a complete list of transformations.The `Dataset` object is a Python iterable. This makes it possible to consume itselements using a for loop:
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dataset = tf.data.Dataset.from_tensor_slices([8, 3, 0, 8, 2, 1])
dataset
for elem in dataset:
print(elem.numpy())
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Apache-2.0
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site/en/guide/data.ipynb
|
zyberg2091/docs
|
Or by explicitly creating a Python iterator using `iter` and consuming itselements using `next`:
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it = iter(dataset)
print(next(it).numpy())
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|
Apache-2.0
|
site/en/guide/data.ipynb
|
zyberg2091/docs
|
Alternatively, dataset elements can be consumed using the `reduce`transformation, which reduces all elements to produce a single result. Thefollowing example illustrates how to use the `reduce` transformation to computethe sum of a dataset of integers.
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print(dataset.reduce(0, lambda state, value: state + value).numpy())
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Apache-2.0
|
site/en/guide/data.ipynb
|
zyberg2091/docs
|
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