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| code
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| output
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-----Hmm, it seems it wasn't so easy in this case. Non-trivial models have non-trivial issues!Remember, our NeMoGPT model sets its self.vocab inside the `setup_train_data` step. But that depends on the vocabulary generated by the train set... which is **not** restored during model restoration (unless you call `setup_train_data` explicitly!).We can quickly resolve this issue by constructing an external data file to enable save and restore support, and NeMo supports that too! We will use the `register_artifact` API in NeMo to support external files being attached to the .nemo checkpoint.
|
class NeMoGPTv2(NeMoGPT):
def setup_training_data(self, train_data_config: OmegaConf):
self.vocab = None
self._train_dl = self._setup_data_loader(train_data_config)
# Save the vocab into a text file for now
with open('vocab.txt', 'w') as f:
for token in self.vocab:
f.write(f"{token}<SEP>")
# This is going to register the file into .nemo!
# When you later use .save_to(), it will copy this file into the tar file.
self.register_artifact(None, 'vocab.txt')
def setup_validation_data(self, val_data_config: OmegaConf):
# This is going to try to find the same file, and if it fails,
# it will use the copy in .nemo
vocab_file = self.register_artifact(None, 'vocab.txt')
with open(vocab_file, 'r') as f:
vocab = []
vocab = f.read().split('<SEP>')[:-1] # the -1 here is for the dangling <SEP> token in the file
self.vocab = vocab
self._validation_dl = self._setup_data_loader(val_data_config)
def setup_test_data(self, test_data_config: OmegaConf):
# This is going to try to find the same file, and if it fails,
# it will use the copy in .nemo
vocab_file = self.register_artifact(None, 'vocab.txt')
with open(vocab_file, 'r') as f:
vocab = []
vocab = f.read().split('<SEP>')[:-1] # the -1 here is for the dangling <SEP> token in the file
self.vocab = vocab
self._test_dl = self._setup_data_loader(test_data_config)
# Let's try creating a model now !
model = NeMoGPTv2(cfg=cfg.model)
# Now let's try to save and restore !
model.save_to('gpt_model.nemo')
temp_model = NeMoGPTv2.restore_from('gpt_model.nemo')
temp_model.setup_multiple_test_data(temp_model.cfg.test_ds)
if torch.cuda.is_available():
cuda = 1
else:
cuda = 0
trainer = ptl.Trainer(gpus=cuda, test_percent_check=1.0)
trainer.test(model)
|
_____no_output_____
|
Apache-2.0
|
tutorials/01_NeMo_Models.ipynb
|
mcdavid109/NeMo
|
Cartesian CoordinatesThe default coordinate system.See`coord_cartesian() `__.
|
import pandas as pd
from lets_plot import *
LetsPlot.setup_html()
df = pd.read_csv('https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv')
p = ggplot(df, aes(x='fl')) + geom_bar()
p1 = p + ggtitle('Default')
p2 = p + coord_cartesian(ylim=[0, 250]) + ggtitle('With Specified Coordinates')
w, h = 400, 300
bunch = GGBunch()
bunch.add_plot(p1, 0, 0, w, h)
bunch.add_plot(p2, w, 0, w, h)
bunch
|
_____no_output_____
|
MIT
|
docs/_downloads/06ac615a764ea75899d9a8dd43c871d1/plot__cartesian_coordinates.ipynb
|
IKupriyanov-HORIS/lets-plot-docs
|
Fundus Analysis - Pathological Myopia
|
!nvidia-smi
|
Wed Jan 20 14:13:47 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 418.67 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |
| N/A 61C P8 10W / 70W | 0MiB / 15079MiB | 0% Default |
| | | ERR! |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
**Import Data from Google Drive**
|
from google.colab import drive
drive.mount('/content/gdrive')
import os
os.environ['KAGGLE_CONFIG_DIR'] = "/content/gdrive/My Drive/Kaggle"
%cd /content/gdrive/My Drive/Kaggle
pwd
|
_____no_output_____
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
**Download Data in Colab**
|
!kaggle datasets download -d andrewmvd/ocular-disease-recognition-odir5k
!ls
|
full_df.csv
imagenet_class_index.json
inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5
inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5
inception_v3_weights_tf_dim_ordering_tf_kernels.h5
inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
kaggle.json
Kuszma.JPG
ocular-disease-recognition-odir5k.zip
ODIR-5K
preprocessed_images
resnet50_weights_tf_dim_ordering_tf_kernels.h5
resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
xception_weights_tf_dim_ordering_tf_kernels.h5
xception_weights_tf_dim_ordering_tf_kernels_notop.h5
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
**Un-zip the Data**
|
!unzip \*.zip && rm *.zip
|
[1;30;43mStreaming output truncated to the last 5000 lines.[0m
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|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Classfication Import Statements
|
import numpy as np
import pandas as pd
import cv2
import random
from tqdm import tqdm
from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import numpy as np
import matplotlib.pyplot as plt
from itertools import cycle
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
from sklearn.metrics import roc_auc_score
import os
df = pd.read_csv("/content/gdrive/My Drive/Kaggle/full_df.csv")
df.head()
def has_myopia(text):
if "pathological myopia" or "myopia" in text:
return 1
else:
return 0
df["left_myopia"] = df["Left-Diagnostic Keywords"].apply(lambda x: has_myopia(x))
df["right_myopia"] = df["Right-Diagnostic Keywords"].apply(lambda x: has_myopia(x))
left_myopia = df.loc[(df.M == 1) & (df.left_myopia == 1)]["Left-Fundus"].values
print(left_myopia[:10])
right_myopia = df.loc[(df.M == 1) & (df.right_myopia == 1)]["Right-Fundus"].values
print(right_myopia[:10])
print("Left Eye Images having myopia: {}".format(len(left_myopia)))
print("Right Eye Images having myopia: {}".format(len(right_myopia)))
left_normal = df.loc[(df.C ==0) & (df["Left-Diagnostic Keywords"] == "normal fundus")]["Left-Fundus"].sample(300,random_state=42).values
right_normal = df.loc[(df.C ==0) & (df["Right-Diagnostic Keywords"] == "normal fundus")]["Right-Fundus"].sample(300,random_state=42).values
print(left_normal[:10])
print(right_normal[:10])
|
['3332_left.jpg' '4059_left.jpg' '69_left.jpg' '2415_left.jpg'
'4176_left.jpg' '2711_left.jpg' '4614_left.jpg' '3174_left.jpg'
'2862_left.jpg' '2424_left.jpg']
['2964_right.jpg' '680_right.jpg' '500_right.jpg' '2368_right.jpg'
'2820_right.jpg' '2769_right.jpg' '2696_right.jpg' '2890_right.jpg'
'940_right.jpg' '2553_right.jpg']
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Left and Right Images Together
|
myopia = np.concatenate((left_myopia,right_myopia),axis=0)
normal = np.concatenate((left_normal,right_normal),axis=0)
print("myopia: {}".format(len(myopia)))
print("Normal: {}".format(len(normal)))
dataset_dir = "/content/gdrive/MyDrive/Kaggle/preprocessed_images/"
image_size = 224
labels = []
dataset = []
def create_dataset(image_category,label):
for img in tqdm(image_category):
image_path = os.path.join(dataset_dir,img)
try:
image = cv2.imread(image_path,cv2.IMREAD_COLOR)
image = cv2.resize(image,(image_size,image_size))
except:
continue
dataset.append([np.array(image),np.array(label)])
random.shuffle(dataset)
return dataset
dataset = create_dataset(myopia,1)
len(dataset)
dataset = create_dataset(normal,0)
len(dataset)
plt.figure(figsize=(12,7))
for i in range(10):
sample = random.choice(range(len(dataset)))
image = dataset[sample][0]
category = dataset[sample][1]
if category == 0:
label = "Normal"
else:
label = "Myopia"
plt.subplot(2,5,i+1)
plt.imshow(image)
plt.xlabel(label)
plt.tight_layout()
x = np.array([i[0] for i in dataset]).reshape(-1,image_size,image_size,3)
y = np.array([i[1] for i in dataset])
x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2)
|
_____no_output_____
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
**Keras Pretrained Models**
|
!kaggle datasets download -d gaborfodor/keras-pretrained-models
!unzip \*.zip && rm *.zip
!ls
pwd
from keras.applications.vgg16 import VGG16, preprocess_input
vgg16_weight_path = '/content/gdrive/MyDrive/Kaggle/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
vgg = VGG16(
weights = vgg16_weight_path,
include_top = False,
input_shape = (224,224,3)
)
for layer in vgg.layers:
layer.trainable = False
|
_____no_output_____
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
**Model**
|
from tensorflow.keras import Sequential
from keras import layers
from tensorflow.keras.layers import Flatten ,Dense
model = Sequential()
model.add(vgg)
model.add(Dense(256, activation='relu'))
model.add(layers.Dropout(rate=0.5))
model.add(Dense(128, activation='sigmoid'))
model.add(layers.Dropout(rate=0.2))
model.add(Dense(128, activation='relu'))
model.add(layers.Dropout(0.1))
model.add(Flatten())
model.add(Dense(1,activation="sigmoid"))
|
_____no_output_____
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Model's Summary
|
model.summary()
model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
history = model.fit(x_train, y_train,
batch_size = 32,
epochs = 30,
validation_data = (x_test, y_test)
)
%cd /content/gdrive/MyDrive/Kaggle
model.save('fundus_model_MYO.h5')
print('saved')
!ls
from sklearn.metrics import confusion_matrix,classification_report,accuracy_score
y_pred = model.predict_classes(x_test)
accuracy_score(y_test,y_pred)
print(classification_report(y_test,y_pred))
|
precision recall f1-score support
0 0.97 0.97 0.97 118
1 0.97 0.97 0.97 118
accuracy 0.97 236
macro avg 0.97 0.97 0.97 236
weighted avg 0.97 0.97 0.97 236
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Predictions
|
# from IPython.display import Image, display
# images = ["/content/gdrive/MyDrive/Kaggle/preprocessed_images/560_right.jpg",
# "/content/gdrive/MyDrive/Kaggle/preprocessed_images/1550_right.jpg",
# "/content/gdrive/MyDrive/Kaggle/preprocessed_images/2330_right.jpg",
# "/content/gdrive/MyDrive/Kaggle/preprocessed_images/0_left.jpg",
# "/content/gdrive/MyDrive/Kaggle/preprocessed_images/179_right.jpg"]
# for image in images:
# display(Image(image, width = 120, height = 120))
# print()
|
_____no_output_____
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Loaded Model
|
pwd
from tensorflow import keras
model = keras.models.load_model('/content/gdrive/MyDrive/Kaggle/fundus_model_MYO.h5')
print('loaded')
model.summary()
from keras.utils.vis_utils import plot_model
plot_model(model, to_file='vgg.png')
from keras.preprocessing.image import load_img
image = load_img("/content/gdrive/MyDrive/Kaggle/preprocessed_images/179_right.jpg", target_size=(224, 224))
from keras.preprocessing.image import img_to_array
# convert the image pixels to a numpy array
image = img_to_array(image)
# reshape data for the model
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
from keras.applications.vgg16 import preprocess_input
# prepare the image for the VGG model
image = preprocess_input(image)
|
_____no_output_____
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Normal Fundus
|
def disease(predic):
if predic > 0.75:
return 'Pathological Myopia'
return 'Normal'
pred = model.predict(image)
status = disease(pred[0])
print("Situation: {}".format(status))
print("Percentage: {}".format(round(int(pred[0]), 1)))
|
Situation: Normal
Percentage: 0
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Myopic Fundus
|
def ready_image(img_path):
image = load_img(img_path, target_size=(224, 224))
image = img_to_array(image)
image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
image = preprocess_input(image)
return image
image = ready_image("/content/gdrive/MyDrive/Kaggle/preprocessed_images/13_right.jpg")
pred = model.predict(image)
status = disease(pred[0])
print("Situation: {}".format(status))
print("Percentage: {}".format(round(int(pred[0]), 1)))
image = ready_image("/content/gdrive/MyDrive/Kaggle/preprocessed_images/233_right.jpg")
pred = model.predict(image)
status = disease(pred[0])
print("Situation: {}".format(status))
print("Percentage: {}".format(round(int(pred[0]), 1)))
|
Situation: Pathological Myopia
Percentage: 0
|
MIT
|
notebooks/Fundus/Single/Fundus_Analysis_Myopia.ipynb
|
mfc2496/EyeSee-Server
|
Started 15:05. Ended
|
cluster = gateway.new_cluster(image=os.environ['JUPYTER_IMAGE'])
|
_____no_output_____
|
MIT
|
eksctl/dask-gateway-test.ipynb
|
salvis2/terraform-aws
|
Build a kernel Matrix
|
# load the structures
frames = read('data/dft-smiles_500.xyz',':')
global_species = []
for frame in frames:
global_species.extend(frame.get_atomic_numbers())
global_species = np.unique(global_species)
# split the structures in 2 sets
frames_train = frames[:300]
frames_test = frames[300:]
# set up the soap parameters
soap_params = dict(rc=3.5, nmax=6, lmax=6, awidth=0.4,
global_species=global_species,nocenters=[])
representation = RawSoapInternal(**soap_params)
# set up the kernel parameters
kernel = KernelSum(KernelPower(zeta = 2),chunk_shape=[100,100])
# compute the soap vectors
rawsoaps = representation.transform(frames_train)
X_train = dict(feature_matrix=rawsoaps,strides=representation.strides)
# compute the soap vectors
rawsoaps = representation.transform(frames_test)
X_test = dict(feature_matrix=rawsoaps,strides=representation.strides)
# compute the square kernel matrix
Kmat = kernel.transform(X_train)
# compute a rectangular kernel matrix
Kmat_rect = kernel.transform(X_test,X_train)
|
_____no_output_____
|
MIT
|
examples/simple_molecule_examples.ipynb
|
felixmusil/ml_tools
|
FPS selection of the samples
|
# load the structures
frames = read('data/dft-smiles_500.xyz',':300')
global_species = []
for frame in frames:
global_species.extend(frame.get_atomic_numbers())
global_species = np.unique(global_species)
# set up the soap parameters
soap_params = dict(rc=3.5, nmax=6, lmax=6, awidth=0.4,
global_species=global_species,nocenters=[])
representation = RawSoapInternal(**soap_params)
# set up the kernel parameters
kernel = KernelSum(KernelPower(zeta = 2),chunk_shape=[100,100])
# compute the soap vectors
rawsoaps = representation.transform(frames)
X = dict(feature_matrix=rawsoaps,strides=representation.strides)
# run the fps selection on the set and plot the minmax distance
Nselect = 250
compressor = FPSFilter(Nselect,kernel,act_on='sample',precompute_kernel=True,disable_pbar=True)
compressor.fit(X,dry_run=True)
compressor.plot()
# select the appropriate number of samples to select
compressor.Nselect = 250
# and compress
X_compressed = compressor.transform(X)
compressor.selected_ids[:compressor.Nselect]
X['feature_matrix'].shape
X_compressed['feature_matrix'].shape
X_compressed['strides'].shape
|
_____no_output_____
|
MIT
|
examples/simple_molecule_examples.ipynb
|
felixmusil/ml_tools
|
FPS selection of the features
|
# load the structures
frames = read('data/dft-smiles_500.xyz',':300')
global_species = []
for frame in frames:
global_species.extend(frame.get_atomic_numbers())
global_species = np.unique(global_species)
# set up the soap parameters
soap_params = dict(rc=3.5, nmax=6, lmax=6, awidth=0.4,
global_species=global_species,nocenters=[])
representation = RawSoapInternal(**soap_params)
# set up the kernel parameters
kernel = KernelPower(zeta = 2)
# compute the soap vectors
X = representation.transform(frames)
# run the fps selection on the set and plot the minmax distance
Nselect = 250
compressor = FPSFilter(Nselect,kernel,act_on='feature',precompute_kernel=True,disable_pbar=True)
compressor.fit(X,dry_run=True)
compressor.plot()
# select the appropriate number of samples to select
compressor.Nselect = 500
# and compress
X_compressed = compressor.transform(X)
compressor.selected_ids[:compressor.Nselect]
|
_____no_output_____
|
MIT
|
examples/simple_molecule_examples.ipynb
|
felixmusil/ml_tools
|
get a cross validation score
|
# load the structures
frames = read('data/dft-smiles_500.xyz',':')
global_species = []
y = []
for frame in frames:
global_species.extend(frame.get_atomic_numbers())
y.append(frame.info['dft_formation_energy_per_atom_in_eV'])
y = np.array(y)
global_species = np.unique(global_species)
# set up the soap parameters
soap_params = dict(rc=3.5, nmax=6, lmax=6, awidth=0.4,
global_species=global_species,nocenters=[])
representation = RawSoapInternal(**soap_params)
# set up the kernel parameters
kernel = KernelSum(KernelPower(zeta = 2),chunk_shape=[100,100])
# set the splitting rational
cv = KFold(n_splits=6,random_state=10,shuffle=True)
# set up the regression model
jitter = 1e-8
krr = KRRFastCV(jitter, 1.,cv)
# compute the soap vectors
rawsoaps = representation.transform(frames)
X = dict(feature_matrix=rawsoaps,strides=representation.strides)
rawsoaps.shape
# compute the kernel matrix for the dataset
Kmat = kernel.transform(X)
# fit the model
krr.fit(Kmat,y)
# get the predictions for each folds
y_pred = krr.predict()
# compute the CV score for the dataset
get_score(y_pred,y)
|
_____no_output_____
|
MIT
|
examples/simple_molecule_examples.ipynb
|
felixmusil/ml_tools
|
LC
|
# load the structures
frames = read('data/dft-smiles_500.xyz',':')
global_species = []
y = []
for frame in frames:
global_species.extend(frame.get_atomic_numbers())
y.append(frame.info['dft_formation_energy_per_atom_in_eV'])
y = np.array(y)
global_species = np.unique(global_species)
# set up the soap parameters
soap_params = dict(rc=3.5, nmax=6, lmax=6, awidth=0.4,
global_species=global_species,nocenters=[])
representation = RawSoapInternal(**soap_params)
# set up the kernel parameters
kernel = KernelSum(KernelPower(zeta = 2),chunk_shape=[100,100])
# set the splitting rational
trainer = TrainerCholesky(memory_efficient=True)
# set up the regression model
jitter = 1e-8
krr = KRR(jitter,1.,trainer)
train_sizes=[20,50,100]
lc = LCSplit(ShuffleSplit, n_repeats=[20,20,20],train_sizes=train_sizes,test_size=100, random_state=10)
rawsoaps = representation.transform(frames)
X = dict(feature_matrix=rawsoaps,strides=representation.strides)
K = kernel.transform(X)
scores = {size:[] for size in train_sizes}
for train,test in tqdm_cs(lc.split(y),total=lc.n_splits):
Ntrain = len(train)
k_train = K[np.ix_(train,train)]
y_train = y[train]
k_test = K[np.ix_(test,train)]
krr.fit(k_train,y_train)
y_pred = krr.predict(k_test)
scores[Ntrain].append(get_score(y_pred,y[test]))
sc_name = 'RMSE'
Ntrains = []
avg_scores = []
for Ntrain, score in scores.items():
avg = 0
for sc in score:
avg += sc[sc_name]
avg /= len(score)
avg_scores.append(avg)
Ntrains.append(Ntrain)
plt.plot(Ntrains,avg_scores,'--o')
plt.xlabel('Number of training samples')
plt.ylabel('Test {}'.format(sc_name))
plt.xscale('log')
plt.yscale('log')
|
_____no_output_____
|
MIT
|
examples/simple_molecule_examples.ipynb
|
felixmusil/ml_tools
|
Table of Contents
|
%load_ext autoreload
%autoreload 2
from argo.workflows.dsl import Workflow
from argo.workflows.dsl import task
from argo.workflows.dsl import template
from argo.workflows.dsl.templates import V1Container
from argo.workflows.dsl.templates import V1alpha1Template
import yaml
from pprint import pprint
from argo.workflows.dsl._utils import sanitize_for_serialization
|
_____no_output_____
|
Apache-2.0
|
examples/hello-world-single-task.ipynb
|
moshewe/argo-python-dsl
|
---
|
!sh -c '[ -f "hello-world-single-task.yaml" ] || curl -LO https://raw.githubusercontent.com/CermakM/argo-python-dsl/master/examples/hello-world-single-task.yaml'
from pathlib import Path
manifest = Path("./hello-world-single-task.yaml").read_text()
print(manifest)
class HelloWorld(Workflow):
@task
def A(self) -> V1alpha1Template:
return self.whalesay()
@template
def whalesay(self) -> V1Container:
container = V1Container(
image="docker/whalesay:latest",
name="whalesay",
command=["cowsay"],
args=["hello world"]
)
return container
wf = HelloWorld()
wf
print(wf.to_yaml())
|
api_version: argoproj.io/v1alpha1
kind: Workflow
metadata:
generate_name: hello-world-
name: hello-world
spec:
entrypoint: main
templates:
- dag:
tasks:
- name: A
template: whalesay
name: main
- container:
args:
- hello world
command:
- cowsay
image: docker/whalesay:latest
name: whalesay
name: whalesay
status: {}
|
Apache-2.0
|
examples/hello-world-single-task.ipynb
|
moshewe/argo-python-dsl
|
---
|
pprint(sanitize_for_serialization(wf))
pprint(yaml.safe_load(manifest))
assert sanitize_for_serialization(wf) == yaml.safe_load(manifest), "Manifests don't match."
|
_____no_output_____
|
Apache-2.0
|
examples/hello-world-single-task.ipynb
|
moshewe/argo-python-dsl
|
How to detect breast cancer with a Support Vector Machine (SVM) and k-nearest neighbours clustering and compare results. Load some packages
|
import scipy
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sklearn
from sklearn import preprocessing
from sklearn.model_selection import train_test_split # cross_validation is deprecated
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn import model_selection
from sklearn.metrics import classification_report, accuracy_score
from pandas.plotting import scatter_matrix
print('NumPy must be 1.14 to run this, it is {}'.format(np.__version__))
print('Python should be version 2.7 or higher, it is {}'.format(sys.version))
|
NumPy must be 1.14 to run this, it is 1.20.3
Python should be version 2.7 or higher, it is 3.9.5 (tags/v3.9.5:0a7dcbd, May 3 2021, 17:27:52) [MSC v.1928 64 bit (AMD64)]
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Read in the dataset from thw UCI data repository.
This details a lot of information from cells, such as their size, clump thickness, shape etc. A pathologist would consider these to determine whether a cell had cancer.
Specifically, we use the read_csv command from pd (pandas) package and supply a url of the dataset and some column names. Then we display the table.
|
# Load Dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"
names = ['id', 'clump_thickness', 'uniform_cell_size', 'uniform_cell_shape',
'marginal_adhesion', 'single_epithelial_size', 'bare_nuclei',
'bland_chromatin', 'normal_nucleoli', 'mitoses', 'class']
df = pd.read_csv(url, names=names)
df.drop(['id'], 1, inplace = True) # We have removed the id field from the dataframe as we would not be running any models on it and we already know that each row represents a single cell.
display(df)
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Get some summary statistics for each of our variables
|
df.describe()
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
The dataset has some missing values. you can use .isnull() to return booleen true false and then tabulate that using .describe to say how many occurrences of true or false there are.
|
df.isnull().describe()
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
If you have missing data, you can replace it.
|
df.replace('?', -9999, inplace = True)
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Class contains information on whether the tumour is benign (class = 2) or malignant (class = 4).
Next we plot a histogram of all variables to show the distrubition.
|
df.hist(figsize = (15,15))
plt.show() # by using plt.show() you render just the plot itself, because python will always display only the last command.
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Look at the relationship between variables with a scatter matrix.
There looks like a pretty strong linear relationship between unifrorm cell shape and uniform cell size.
If you look at the cells representing comparisons with class (our outcome variable), it appears that there are a range of values for each of the items.
|
scatter_matrix(df, figsize = (15,15))
plt.show() # by using plt.show() you render just the plot itself, because python will always display only the last command.
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Models Create training and testing datasets.
We need to keep some of the data back to validate the model, seeing how well it generalises to other data.
x data will contain all the potential explanatory variables (called features I think in this context)
y will contain the outcome data (called label in ML)
|
X_df = np.array(df.drop(['class'], 1)) # this will create a variable called X_df which is df except class
y_df = np.array(df['class']) # this is just the class field
X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.2) # split the dataset into four, two with features, two with labels (and choose 20% of the data for testing (validation))
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Add a seed to make the data reproducible (this will change the results a little each time we run the model)
|
seed = 8
scoring = 'accuracy'
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Create training models make an empty list then append
|
models = []
models.append(('KNN', KNeighborsClassifier(n_neighbors = 5))) # You can alter the number of neighbours
models.append(('SVM', SVC()))
results = [] # also create lists for results and names. We use this to print out the results
names = []
|
_____no_output_____
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
Evaluate each model in turn
|
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state = seed, shuffle = True)
cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
|
KNN: 0.967825 (0.023671)
SVM: 0.638539 (0.053601)
|
MIT
|
ML_breast_cancer_detection_with_SVM_KNN.ipynb
|
psychty/jubilant-potato
|
created by Ignacio Oguiza - email: [email protected] ROCKET (RandOm Convolutional KErnel Transform) is a new Time Series Classification (TSC) method that has just been released (Oct 29th, 2019), and has achieved **state-of-the-art performance on the UCR univariate time series classification datasets, surpassing HIVE-COTE (the previous state of the art since 2017) in accuracy, with exceptional speed compared to other traditional DL methods.** To achieve these 2 things at once is **VERY IMPRESSIVE**. ROCKET is certainly a new TSC method you should try.Authors:Dempster, A., Petitjean, F., & Webb, G. I. (2019). ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. arXiv preprint arXiv:1910.13051.[paper](https://arxiv.org/pdf/1910.13051)There are 2 main limitations to the original ROCKET method though:- Released code doesn't handle multivariate data- It doesn't run on a GPU, so it's slow when used with a large datasetsIn this notebook you will learn: - how you can use the original ROCKET method- you will also learn about a new ROCKET version I have developed in Pytorch, that handles both **univariate and multivariate** data, and uses **GPU**- you will see how you can integrate the ROCKET features with fastai or other classifiers Import libraries 📚
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# ## NOTE: UNCOMMENT AND RUN THIS CELL IF YOU NEED TO INSTALL/ UPGRADE TSAI
# stable = False # True: latest version from github, False: stable version in pip
# if stable:
# !pip install -Uqq tsai
# else:
# !pip install -Uqq git+https://github.com/timeseriesAI/tsai.git
# ## NOTE: REMEMBER TO RESTART YOUR RUNTIME ONCE THE INSTALLATION IS FINISHED
from tsai.all import *
print('tsai :', tsai.__version__)
print('fastai :', fastai.__version__)
print('fastcore :', fastcore.__version__)
print('torch :', torch.__version__)
|
/usr/local/lib/python3.6/dist-packages/numba/np/ufunc/parallel.py:363: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.
warnings.warn(problem)
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
How to use the original ROCKET method? 🚀 ROCKET is applied in 2 phases:1. Generate features from each time series: ROCKET calculates 20k features from each time series, independently of the sequence length. 2. Apply a classifier to those calculated features. Those features are then used by the classifier of your choice. In the original code they use 2 simple linear classifiers: RidgeClassifierCV and Logistic Regression, but you can use any classifier. 1️⃣ Generate featuresLet's first generate the features. We'll import data from a UCR Time Series dataset.The original method requires the time series to be in a 2d array of shape (samples, len). Remember than only univariate sequences are allow in this original method.
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X_train, y_train, X_valid, y_valid = get_UCR_data('OliveOil')
seq_len = X_train.shape[-1]
X_train = X_train[:, 0]
X_valid = X_valid[:, 0]
labels = np.unique(y_train)
transform = {}
for i, l in enumerate(labels): transform[l] = i
y_train = np.vectorize(transform.get)(y_train)
y_valid = np.vectorize(transform.get)(y_valid)
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_____no_output_____
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Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
Now we normalize the data to mean 0 and std 1 **'per sample'** (recommended by the authors), that is they set each sample to mean 0 and std 1).
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X_train = (X_train - X_train.mean(axis = 1, keepdims = True)) / (X_train.std(axis = 1, keepdims = True) + 1e-8)
X_valid = (X_valid - X_valid.mean(axis = 1, keepdims = True)) / (X_valid.std(axis = 1, keepdims = True) + 1e-8)
X_train.mean(axis = 1, keepdims = True).shape
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
To generate the features, we first need to create the 10k random kernels that will be used to process the data.
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kernels = generate_kernels(seq_len, 10000)
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_____no_output_____
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Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
Now we apply those ramdom kernels to the data
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X_train_tfm = apply_kernels(X_train, kernels)
X_valid_tfm = apply_kernels(X_valid, kernels)
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
2️⃣ Apply a classifierSo now we have the features, and we are ready to apply a classifier. Let's use a simple, linear RidgeClassifierCV as they propose in the paper. We first instantiate it. Note:alphas: Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to C^-1 in other linear models such as LogisticRegression or LinearSVC.
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from sklearn.linear_model import RidgeClassifierCV
classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 7), normalize=True)
classifier.fit(X_train_tfm, y_train)
classifier.score(X_valid_tfm, y_valid)
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
☣️ **This is pretty impressive! It matches or exceeds the state-of-the-art performance without any fine tuning in <2 seconds!!!**
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kernels = generate_kernels(seq_len, 10000)
X_train_tfm = apply_kernels(X_train, kernels)
X_valid_tfm = apply_kernels(X_valid, kernels)
from sklearn.linear_model import RidgeClassifierCV
classifier = RidgeClassifierCV(alphas=np.logspace(-3, 3, 7), normalize=True)
classifier.fit(X_train_tfm, y_train)
classifier.score(X_valid_tfm, y_valid)
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
⚠️ Bear in mind that this process is not deterministic since there is randomness involved in the kernels. In thiis case, performance may vary between .9 to .933 How to use ROCKET with large and/ or multivariate datasets on GPU? - Recommended ⭐️ As stated before, the current ROCKET method doesn't support multivariate time series or GPU. This may be a drawback in some cases. To overcome both limitations I've created a multivariate ROCKET on GPU in Pytorch. 1️⃣ Generate featuresFirst you prepare the input data and normalize it per sample. The input to ROCKET Pytorch is a 3d tensor of shape (samples, vars, len), preferrable on gpu. The way to use ROCKET in Pytorch is the following:* Create a dataset as you would normally do in `tsai`. * Create a TSDataLoaders with the following kwargs: * drop_last=False. In this way we get features for every input sample. * shuffle_train=False * batch_tfms=[TSStandardize(by_sample=True)] so that input is normalized by sample, as recommended by the authors
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X, y, splits = get_UCR_data('HandMovementDirection', split_data=False)
tfms = [None, [Categorize()]]
batch_tfms = [TSStandardize(by_sample=True)]
dls = get_ts_dls(X, y, splits=splits, tfms=tfms, drop_last=False, shuffle_train=False, batch_tfms=batch_tfms, bs=10_000)
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
☣️☣️ You will be able to create a dls (TSDataLoaders) object with unusually large batch sizes. I've tested it with a large dataset and a batch size = 100_000 and it worked fine. This is because ROCKET is not a usual Deep Learning model. It just applies convolutions (kernels) one at a time to create the features. Instantiate a rocket model with the desired n_kernels (authors use 10_000) and kernel sizes (7, 9 and 11 in the original paper).
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model = build_ts_model(ROCKET, dls=dls) # n_kernels=10_000, kss=[7, 9, 11] set by default, but you can pass other values as kwargs
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
Now generate rocket features for the entire train and valid datasets using the create_rocket_features convenience function `create_rocket_features`. And we now transform the original data, creating 20k features per sample
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X_train, y_train = create_rocket_features(dls.train, model)
X_valid, y_valid = create_rocket_features(dls.valid, model)
X_train.shape, X_valid.shape
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
2️⃣ Apply a classifier Once you build the 20k features per sample, you can use them to train any classifier of your choice. RidgeClassifierCV And now you apply a classifier of your choice. With RidgeClassifierCV in particular, there's no need to normalize the calculated features before passing them to the classifier, as it does it internally (if normalize is set to True as recommended by the authors).
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from sklearn.linear_model import RidgeClassifierCV
ridge = RidgeClassifierCV(alphas=np.logspace(-8, 8, 17), normalize=True)
ridge.fit(X_train, y_train)
print(f'alpha: {ridge.alpha_:.2E} train: {ridge.score(X_train, y_train):.5f} valid: {ridge.score(X_valid, y_valid):.5f}')
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alpha: 1.00E+01 train: 1.00000 valid: 0.50000
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
This result is amazing!! The previous state of the art (Inceptiontime) was .37837 Logistic Regression In the case of other classifiers (like Logistic Regression), the authors recommend a per-feature normalization.
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eps = 1e-6
Cs = np.logspace(-5, 5, 11)
from sklearn.linear_model import LogisticRegression
best_loss = np.inf
for i, C in enumerate(Cs):
f_mean = X_train.mean(axis=0, keepdims=True)
f_std = X_train.std(axis=0, keepdims=True) + eps # epsilon to avoid dividing by 0
X_train_tfm2 = (X_train - f_mean) / f_std
X_valid_tfm2 = (X_valid - f_mean) / f_std
classifier = LogisticRegression(penalty='l2', C=C, n_jobs=-1)
classifier.fit(X_train_tfm2, y_train)
probas = classifier.predict_proba(X_train_tfm2)
loss = nn.CrossEntropyLoss()(torch.tensor(probas), torch.tensor(y_train)).item()
train_score = classifier.score(X_train_tfm2, y_train)
val_score = classifier.score(X_valid_tfm2, y_valid)
if loss < best_loss:
best_eps = eps
best_C = C
best_loss = loss
best_train_score = train_score
best_val_score = val_score
print('{:2} eps: {:.2E} C: {:.2E} loss: {:.5f} train_acc: {:.5f} valid_acc: {:.5f}'.format(
i, eps, C, loss, train_score, val_score))
print('\nBest result:')
print('eps: {:.2E} C: {:.2E} train_loss: {:.5f} train_acc: {:.5f} valid_acc: {:.5f}'.format(
best_eps, best_C, best_loss, best_train_score, best_val_score))
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0 eps: 1.00E-06 C: 1.00E-05 loss: 1.35151 train_acc: 0.80000 valid_acc: 0.41892
1 eps: 1.00E-06 C: 1.00E-04 loss: 1.15433 train_acc: 1.00000 valid_acc: 0.45946
2 eps: 1.00E-06 C: 1.00E-03 loss: 0.85364 train_acc: 1.00000 valid_acc: 0.48649
3 eps: 1.00E-06 C: 1.00E-02 loss: 0.76183 train_acc: 1.00000 valid_acc: 0.48649
4 eps: 1.00E-06 C: 1.00E-01 loss: 0.74625 train_acc: 1.00000 valid_acc: 0.48649
5 eps: 1.00E-06 C: 1.00E+00 loss: 0.74401 train_acc: 1.00000 valid_acc: 0.48649
6 eps: 1.00E-06 C: 1.00E+01 loss: 0.74371 train_acc: 1.00000 valid_acc: 0.50000
7 eps: 1.00E-06 C: 1.00E+02 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
8 eps: 1.00E-06 C: 1.00E+03 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
9 eps: 1.00E-06 C: 1.00E+04 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
10 eps: 1.00E-06 C: 1.00E+05 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
Best result:
eps: 1.00E-06 C: 1.00E+05 train_loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
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Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
☣️ Note: Epsilon has a large impact on the result. You can actually test several values to find the one that best fits your problem, but bear in mind you can only select C and epsilon based on train data!!! RandomSearch One way to do this would be to perform a random search using several epsilon and C values
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n_tests = 10
epss = np.logspace(-8, 0, 9)
Cs = np.logspace(-5, 5, 11)
from sklearn.linear_model import LogisticRegression
best_loss = np.inf
for i in range(n_tests):
eps = np.random.choice(epss)
C = np.random.choice(Cs)
f_mean = X_train.mean(axis=0, keepdims=True)
f_std = X_train.std(axis=0, keepdims=True) + eps # epsilon
X_train_tfm2 = (X_train - f_mean) / f_std
X_valid_tfm2 = (X_valid - f_mean) / f_std
classifier = LogisticRegression(penalty='l2', C=C, n_jobs=-1)
classifier.fit(X_train_tfm2, y_train)
probas = classifier.predict_proba(X_train_tfm2)
loss = nn.CrossEntropyLoss()(torch.tensor(probas), torch.tensor(y_train)).item()
train_score = classifier.score(X_train_tfm2, y_train)
val_score = classifier.score(X_valid_tfm2, y_valid)
if loss < best_loss:
best_eps = eps
best_C = C
best_loss = loss
best_train_score = train_score
best_val_score = val_score
print('{:2} eps: {:.2E} C: {:.2E} loss: {:.5f} train_acc: {:.5f} valid_acc: {:.5f}'.format(
i, eps, C, loss, train_score, val_score))
print('\nBest result:')
print('eps: {:.2E} C: {:.2E} train_loss: {:.5f} train_acc: {:.5f} valid_acc: {:.5f}'.format(
best_eps, best_C, best_loss, best_train_score, best_val_score))
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0 eps: 1.00E-03 C: 1.00E-03 loss: 0.85501 train_acc: 1.00000 valid_acc: 0.48649
1 eps: 1.00E-02 C: 1.00E-03 loss: 0.86484 train_acc: 1.00000 valid_acc: 0.47297
2 eps: 1.00E-06 C: 1.00E+03 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
3 eps: 1.00E-04 C: 1.00E-05 loss: 1.35157 train_acc: 0.80000 valid_acc: 0.41892
4 eps: 1.00E-07 C: 1.00E+00 loss: 0.74401 train_acc: 1.00000 valid_acc: 0.48649
5 eps: 1.00E-07 C: 1.00E-03 loss: 0.85364 train_acc: 1.00000 valid_acc: 0.48649
6 eps: 1.00E-01 C: 1.00E-05 loss: 1.36582 train_acc: 0.93125 valid_acc: 0.40541
7 eps: 1.00E-06 C: 1.00E+02 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
8 eps: 1.00E-07 C: 1.00E+05 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
9 eps: 1.00E-03 C: 1.00E+02 loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
Best result:
eps: 1.00E-07 C: 1.00E+05 train_loss: 0.74367 train_acc: 1.00000 valid_acc: 0.48649
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Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
In general, the original method may be a bit faster than the GPU method, but for larger datasets, there's a great benefit in using the GPU version. In addition to this, I have also run the code on the TSC UCR multivariate datasets (all the ones that don't contain nan values), and the results are also very good, beating the previous state-of-the-art in this category as well by a large margin. For example, ROCKET reduces InceptionTime errors by 26% on average. Fastai classifier head
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X = concat(X_train, X_valid)
y = concat(y_train, y_valid)
splits = get_predefined_splits(X_train, X_valid)
tfms = [None, [Categorize()]]
dsets = TSDatasets(X, y, tfms=tfms, splits=splits)
dls = TSDataLoaders.from_dsets(dsets.train, dsets.valid, bs=128, batch_tfms=[TSStandardize(by_var=True)])# per feature normalization
dls.show_batch()
def lin_zero_init(layer):
if isinstance(layer, nn.Linear):
nn.init.constant_(layer.weight.data, 0.)
if layer.bias is not None: nn.init.constant_(layer.bias.data, 0.)
model = create_mlp_head(dls.vars, dls.c, dls.len)
model.apply(lin_zero_init)
learn = Learner(dls, model, metrics=accuracy, cbs=ShowGraph())
learn.fit_one_cycle(50, lr_max=1e-4)
learn.plot_metrics()
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
XGBoost
|
eps = 1e-6
# normalize 'per feature'
f_mean = X_train.mean(axis=0, keepdims=True)
f_std = X_train.std(axis=0, keepdims=True) + eps
X_train_norm = (X_train - f_mean) / f_std
X_valid_norm = (X_valid - f_mean) / f_std
import xgboost as xgb
classifier = xgb.XGBClassifier(max_depth=3,
learning_rate=0.1,
n_estimators=100,
verbosity=1,
objective='binary:logistic',
booster='gbtree',
tree_method='auto',
n_jobs=-1,
gpu_id=default_device().index,
gamma=0,
min_child_weight=1,
max_delta_step=0,
subsample=.5,
colsample_bytree=1,
colsample_bylevel=1,
colsample_bynode=1,
reg_alpha=0,
reg_lambda=1,
scale_pos_weight=1,
base_score=0.5,
random_state=0,
missing=None)
classifier.fit(X_train_norm, y_train)
preds = classifier.predict(X_valid_norm)
(preds == y_valid).mean()
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_____no_output_____
|
Apache-2.0
|
tutorial_nbs/02_ROCKET_a_new_SOTA_classifier.ipynb
|
duyniem/tsai
|
Brownian process in stock price dynamics Brownian Moton:source: https://en.wikipedia.org/wiki/Brownian_motion A **random-walk** can be seen as a **motion** resulting from a succession of discrete **random steps**.The random-walk after the i-th steps is:\begin{equation}\tag{1}X_{i} = X_{i-1} + \epsilon_{i} \end{equation}being $X_{i=0} = X_{0} = 0$ the starting point and $\epsilon_{i}$ a random variable
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# conda install -c anaconda pandas-datareader
# pip install pandas-datareader
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# Possible steps
steps = [-1,1] # backward and forward of 1 units
# Nr of steps n_steps
n_steps = 100
# Initialise the random walk variable X
X = np.zeros(n_steps) #<--- numpy array of (N=n_steps) zeros
# Fill in X according to eq. 1
for i in range(1,n_steps):
X[i]= X[i-1] + np.random.choice(steps)#<--- from 1 to fulfill Initial condition
# Faster alternative
def random_walk(steps,n_steps):
random_steps = np.random.choice(steps,size=n_steps)
X = random_steps.cumsum()
X[0] = 0 # <--- initial position
return X
for i in range(4):
plt.plot(random_walk(steps,n_steps))
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_____no_output_____
|
MIT
|
Brownian_motion.ipynb
|
CarSomma/Brownian-Process
|
**If we repeat the experiment where does the man end up in average?**
|
# Repeat the random walk n_trials time
# Record the last position for each trial
def monte_random_walk(n_steps,steps,n_trials):
X_fin = np.zeros(n_trials)#<-- X_fin numpy array of (N=n_trial) zeros
for i in range(n_trials):
X_fin[i] =random_walk(steps,n_steps)[-1]
return X_fin
n_trial = 20000
steps = [-1,1]
n_steps = 100
X_fin = monte_random_walk(n_steps,steps,n_trial)
# Plot the distribution of X_fin
width_bin = 4
n_bins = int(np.ceil((np.max(X_fin)-np.min(X_fin))/width_bin))
sns.distplot(X_fin,kde=True,bins=n_bins);
plt.xlabel('Final position');
np.std(X_fin)
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_____no_output_____
|
MIT
|
Brownian_motion.ipynb
|
CarSomma/Brownian-Process
|
We can see a Brownian process $B(t)$ as a **continuous Gaussian** random walk. **Gaussian & continuous**: we divide the observation time $t$ into $N$ small timestep $\Delta t$, so that $t=N\cdot\Delta t$.For any time $t_i=i\cdot\Delta t$, the change in $B$ is normally distributed:$$ B_{i+1}-B_i \sim \sqrt{\Delta t}\cdot N(0,1)$$Taking time step $\Delta t$ smaller and smaller will make B a continuous random walk.
|
def brownian_motion(T,N,n_trials,random_seed = None):
np.random.seed(random_seed)
dt = T/N
random_steps = np.sqrt(dt)*np.random.normal(loc = 0,scale = 1,size = (N,n_trials))
random_steps[0,:] = 0
X = np.cumsum(random_steps,axis=0)
return X
T=7
N=100
n_trials=2000
random_seed = 1
dt=T/N
dt
X= brownian_motion(T,N,n_trials,random_seed)
# Last step
X_fin = X[-1,:]
plt.plot(X);
# Plot the distribution of X_fin
width_bin = .51
n_bins = int(np.ceil((np.max(X_fin)-np.min(X_fin))/width_bin))
sns.distplot(X_fin,bins=n_bins);
|
_____no_output_____
|
MIT
|
Brownian_motion.ipynb
|
CarSomma/Brownian-Process
|
Connection to stock-priceThe dynamics of stock-prices can be modeled by the following equation:\begin{equation}\tag{2}\Delta S_{t} = \mu S_{t} \Delta t + \sigma S_{t}\Delta B_{t}\end{equation}being:$S$ the stock price,$\mu$ the drift coefficient (a.k.a the mean of returns),$\sigma$ the diffusion coefficient (a.k.a the standard deviation of returns),$B$ the brownian motion.The eq. (2) admits the following solution:\begin{equation}\tag{3}S(t) = S_{0} \cdot e^{[(\mu - \sigma^2/2)\cdot t + \sigma \cdot B_{t}] } \end{equation}
|
def stock_price(N,S0,u,sigma,T,n_trials,random_seed = None):
"""
N: number of intervals
S0: initial stock price
u: mean of returns over some period
sigma: volatility a.k.a. standard deviation of returns
random_seed: seed for pseudorandom generator
T: observation time
m: number of brownian path
"""
dt = T/N
t = np.arange(0.,T,dt)
t=t[:,np.newaxis]
drift = (u - (sigma/np.sqrt(2))**2)*t
shock = sigma * brownian_motion(T,N,n_trials,random_seed = None)
S = S0*np.exp(drift + shock)
return t, S
|
_____no_output_____
|
MIT
|
Brownian_motion.ipynb
|
CarSomma/Brownian-Process
|
Scraping from Yahoo Finance
|
from pandas_datareader import data as scraper
import pandas as pd
symbol = 'FB' # 'FB'Facebook, 'FCA.MI' FIAT Crysler, 'AAPL' Apple
start_date = '2020-01-01'
end_date = '2020-12-31'
df = scraper.DataReader(symbol, 'yahoo', start_date, end_date)
df.head()
df.describe()
#close price
close_price = df['Close']
close_price.plot();
plt.ylabel('Price $');
# Calculate the daily percentage return
daily_return= (close_price.pct_change() )
daily_return.plot(label='Daily Return')
(close_price*.002).plot(label='Close Price');
plt.legend();
# Plot the distribution of daily_return
width_bin = .01
n_bins = int(np.ceil((np.max(daily_return)-np.min(daily_return))/width_bin))
sns.distplot(daily_return,bins=n_bins);
plt.title("Daily returns on FB, 2020");
# compute the return mu and the sigma
mu = np.mean(daily_return)
sigma = np.std(daily_return)
print(f'Mean of daily-returns μ: {round(mu,4)*100} %')
print('')
print(f'Volatility σ: {round(sigma,3)}')
# Parameters simulation
N = 5000 # <--- lenght of each trials
T=252 # <--- # days of a business year
S0=close_price[0] # <--- Initial close-price
n_trials=25500 # <--- # of trials
T/N # <--- Δt about 0.05
# Extracting stock price pathways and time vector from the model
t,model_S = stock_price(N,S0,mu,sigma,T,n_trials,random_seed = 42)
#model_S.shape
# Define other two time range
t2=np.arange(0,253,1)
# Plot simulated and actual stock-prizes
plt.plot(t,model_S);
#plt.plot(t3,close_price[-12:],linewidth=3,c='k');
plt.plot(t2,close_price[:],linewidth=3,c='k');
plt.xlabel('Days');
plt.ylabel('Stock Price');
# Compute final predicted stock-price
S_fin = model_S[-1,:]
# Calculate mean and std from S_fin
mean = np.mean(S_fin)
median=np.median(S_fin)
std_ = np.std(S_fin)
min_ = np.min(S_fin)
max_ = np.max(S_fin)
print('*******************')
print(f' * Statistics *')
print('*******************\n')
print(f'Min: {round(min_)} $')
print(f'Max: {round(max_)} $')
print(f'Median: {round(median)} $')
print(f'Mean: {round(mean)} $')
print(f'Standard deviation: {round(std_)} $')
# Plot the simulated final stock-price
sns.distplot(S_fin);
plt.plot([median,median], [0, .02], 'k-.', lw=6,label='median')
plt.plot([mean,mean], [0, .02], 'b-.', lw=2,label='mean')
plt.plot([close_price[-1],close_price[-1]], [0, .02], 'g-', lw=2,label='actual prize')
plt.ylim(top=0.004);
plt.xlim(left=-100,right=1200)
plt.legend();
plt.title('Montecarlo Simulation on Facebook Stock-Price');
plt.xlabel('Stock price $');
from scipy.stats import norm,lognorm,t
def lognorm_fit(data_,x_min,x_max,dx):
# Fits the datas with a log-norm distribution
params = lognorm.fit(data_)
shape, mean, std = params
# Generate a log-norm probability distribution function pdf
x = np.arange(x_min,x_max,dx)
lnd = lognorm(s=shape,loc=mean,scale=std)# <--- initialise the log-norm distribution
lognormal_pdf =lnd.pdf(x)
# Calculate the mode of distribution
index_max = np.argmax(lognormal_pdf) #np.where(lognormal_pdf == np.max(lognormal_pdf))
mode =x[index_max]
return lnd,lognormal_pdf, mode,x
x_min=0
x_max=5000
dx=.1
# Distribution and mode
lnd_S,lognormal_pdf_S,mode_S,x = lognorm_fit(S_fin,x_min,x_max,dx)
# Plot the simulated final stock-price
sns.distplot(S_fin);
sns.lineplot(x,lognormal_pdf_S,label = 'log-normal')
plt.plot([mode_S,mode_S],[0,.02],'r-.',label= 'mode')
plt.plot([median,median], [0, .02], 'k-.', lw=6,label='median')
plt.plot([mean,mean], [0, .02], 'b-.', lw=2,label='mean')
plt.plot([close_price[-1],close_price[-1]], [0, .02], 'g-', lw=2,label='actual prize')
plt.ylim(top=0.004);
plt.xlim(left=-100,right=1200)
plt.legend();
plt.title('Montecarlo Simulation on Facebook Stock-Price');
plt.xlabel('Stock price $');
|
_____no_output_____
|
MIT
|
Brownian_motion.ipynb
|
CarSomma/Brownian-Process
|
what is the probability of having a loss after one year?
|
# Annual Return
annual_return_pct = (S_fin -S0)/S0
# Calculate mean and std from S_fin
mean_ar = np.mean(annual_return_pct)
median_ar=np.median(annual_return_pct)
std_ar = np.std(annual_return_pct)
min_ar = np.min(annual_return_pct)
max_ar = np.max(annual_return_pct)
print('*******************')
print(f' * Statistics *')
print('*******************\n')
print(f'Min: {round(min_ar,2)} %')
print(f'Max: {round(max_ar,2)} %')
print(f'Median: {round(median_ar,2)} %')
print(f'Mean: {round(mean_ar,2)} %')
print(f'Standard deviation: {round(std_ar,2)} %')
# Plot distribution of simulated annual return
sns.distplot(annual_return_pct);
plt.ylim(top=0.8);
plt.xlim(left=-3,right=6)
plt.title('Montecarlo Simulation on Facebook Stock-Price');
plt.xlabel('Annual Return % ');
|
_____no_output_____
|
MIT
|
Brownian_motion.ipynb
|
CarSomma/Brownian-Process
|
Analysis of underlying distribution
|
x_min=-5
x_max=6
dx=.001
# Distribution and mode
lnd_ar,lognormal_pdf_ar,mode_ar,x_ar = lognorm_fit(annual_return_pct,x_min,x_max,dx)
# Plot distribution of simulated annual return
sns.distplot(annual_return_pct);
sns.lineplot(x_ar,lognormal_pdf_ar,label = 'log-normal');
plt.plot([mode_ar,mode_ar],[0,.9],'k-.',label= 'mode');
plt.ylim(top=0.8);
plt.xlim(left=-3,right=6)
plt.legend();
plt.text(x=2,y=.5,s=f'mode @ {round(mode_ar,3)}',)
plt.title('Montecarlo Simulation on Facebook Stock-Price');
plt.xlabel('Annual Return % ');
# Cumulative distribution Function CDF (probability of obtaining a value equal or smaller than the given value)
cdf = lnd_ar.cdf(x_ar) # <--- cumulative
# Plot CDF SF function
sns.lineplot(x_ar,cdf,label='CDF');
plt.plot([0,0], [0, 1], 'r-', lw=2,label='No returns');
plt.legend();
plt.xlabel('Annual Return %');
def get_prob(value_return1,value_return2=None):
mask_1 = (x_ar<=value_return1)
if value_return2==None:
prob = round(np.max(cdf[mask_1])*100,2)
else:
mask_2 = (x_ar<=value_return2)
area1 = np.max(cdf[mask_1])*100
area2 = np.max(cdf[mask_2])*100
prob = np.round(area2 - area1,2)
return prob
print('**************************************')
print(' * Results *')
print('**************************************\n')
print(' Return_1 Return_2 Probability\n')
print(f'Loss {get_prob(-0.0001)} %')
print(f'Gain 0.1% 1% {get_prob(0.1,1)} % ')
print(f'Gain 1% 2% {get_prob(1,2)} % ')
|
**************************************
* Results *
**************************************
Return_1 Return_2 Probability
Loss 28.48 %
Gain 0.1% 1% 46.65 %
Gain 1% 2% 13.91 %
|
MIT
|
Brownian_motion.ipynb
|
CarSomma/Brownian-Process
|
City street network orientationsCompare the spatial orientations of city street networks with OSMnx. - [Overview of OSMnx](http://geoffboeing.com/2016/11/osmnx-python-street-networks/) - [GitHub repo](https://github.com/gboeing/osmnx) - [Examples, demos, tutorials](https://github.com/gboeing/osmnx-examples) - [Documentation](https://osmnx.readthedocs.io/en/stable/) - [Journal article/citation](http://geoffboeing.com/publications/osmnx-complex-street-networks/)
|
import matplotlib.pyplot as plt
import numpy as np
import osmnx as ox
import pandas as pd
ox.config(log_console=True, use_cache=True)
weight_by_length = False
ox.__version__
# define the study sites as label : query
places = {'Atlanta' : 'Atlanta, GA, USA',
'Boston' : 'Boston, MA, USA',
'Buffalo' : 'Buffalo, NY, USA',
'Charlotte' : 'Charlotte, NC, USA',
'Chicago' : 'Chicago, IL, USA',
'Cleveland' : 'Cleveland, OH, USA',
'Dallas' : 'Dallas, TX, USA',
'Houston' : 'Houston, TX, USA',
'Denver' : 'Denver, CO, USA',
'Detroit' : 'Detroit, MI, USA',
'Las Vegas' : 'Las Vegas, NV, USA',
'Los Angeles' : {'city':'Los Angeles', 'state':'CA', 'country':'USA'},
'Manhattan' : 'Manhattan, NYC, NY, USA',
'Miami' : 'Miami, FL, USA',
'Minneapolis' : 'Minneapolis, MN, USA',
'Orlando' : 'Orlando, FL, USA',
'Philadelphia' : 'Philadelphia, PA, USA',
'Phoenix' : 'Phoenix, AZ, USA',
'Portland' : 'Portland, OR, USA',
'Sacramento' : 'Sacramento, CA, USA',
'San Francisco' : {'city':'San Francisco', 'state':'CA', 'country':'USA'},
'Seattle' : 'Seattle, WA, USA',
'St Louis' : 'St. Louis, MO, USA',
'Tampa' : 'Tampa, FL, USA',
'Washington' : 'Washington, DC, USA'}
places = {'Accra' : 'Accra Metropolitan, Greater Accra Region, Ghana'}
# verify OSMnx geocodes each query to what you expect
gdf = ox.gdf_from_places(places.values())
gdf
|
_____no_output_____
|
MIT
|
notebooks/17-street-network-orientations.ipynb
|
baerbelblume/osmnx-examples
|
Get the street networks and their edge bearings
|
def reverse_bearing(x):
return x + 180 if x < 180 else x - 180
bearings = {}
for place in sorted(places.keys()):
# get the graph
query = places[place]
G = ox.graph_from_place(query, network_type='drive')
# calculate edge bearings
Gu = ox.add_edge_bearings(ox.get_undirected(G))
if weight_by_length:
# weight bearings by length (meters)
city_bearings = []
for u, v, k, d in Gu.edges(keys=True, data=True):
city_bearings.extend([d['bearing']] * int(d['length']))
b = pd.Series(city_bearings)
bearings[place] = pd.concat([b, b.map(reverse_bearing)]).reset_index(drop='True')
else:
# don't weight bearings, just take one value per street segment
b = pd.Series([d['bearing'] for u, v, k, d in Gu.edges(keys=True, data=True)])
bearings[place] = pd.concat([b, b.map(reverse_bearing)]).reset_index(drop='True')
|
_____no_output_____
|
MIT
|
notebooks/17-street-network-orientations.ipynb
|
baerbelblume/osmnx-examples
|
Visualize it
|
def count_and_merge(n, bearings):
# make twice as many bins as desired, then merge them in pairs
# prevents bin-edge effects around common values like 0° and 90°
n = n * 2
bins = np.arange(n + 1) * 360 / n
count, _ = np.histogram(bearings, bins=bins)
# move the last bin to the front, so eg 0.01° and 359.99° will be binned together
count = np.roll(count, 1)
return count[::2] + count[1::2]
# function to draw a polar histogram for a set of edge bearings
def polar_plot(ax, bearings, n=36, title=''):
bins = np.arange(n + 1) * 360 / n
count = count_and_merge(n, bearings)
_, division = np.histogram(bearings, bins=bins)
frequency = count / count.sum()
division = division[0:-1]
width = 2 * np.pi / n
ax.set_theta_zero_location('N')
ax.set_theta_direction('clockwise')
x = division * np.pi / 180
bars = ax.bar(x, height=frequency, width=width, align='center', bottom=0, zorder=2,
color='#003366', edgecolor='k', linewidth=0.5, alpha=0.7)
ax.set_ylim(top=frequency.max())
title_font = {'family':'Century Gothic', 'size':24, 'weight':'bold'}
xtick_font = {'family':'Century Gothic', 'size':10, 'weight':'bold', 'alpha':1.0, 'zorder':3}
ytick_font = {'family':'Century Gothic', 'size': 9, 'weight':'bold', 'alpha':0.2, 'zorder':3}
ax.set_title(title.upper(), y=1.05, fontdict=title_font)
ax.set_yticks(np.linspace(0, max(ax.get_ylim()), 5))
yticklabels = ['{:.2f}'.format(y) for y in ax.get_yticks()]
yticklabels[0] = ''
ax.set_yticklabels(labels=yticklabels, fontdict=ytick_font)
xticklabels = ['N', '', 'E', '', 'S', '', 'W', '']
ax.set_xticklabels(labels=xticklabels, fontdict=xtick_font)
ax.tick_params(axis='x', which='major', pad=-2)
# create figure and axes
n = len(places)
ncols = int(np.ceil(np.sqrt(n)))
nrows = int(np.ceil(n / ncols))
figsize = (ncols * 5, nrows * 5)
fig, axes = plt.subplots(nrows, ncols, figsize=figsize, subplot_kw={'projection':'polar'})
# plot each city's polar histogram
for ax, place in zip(axes.flat, sorted(places.keys())):
polar_plot(ax, bearings[place].dropna(), title=place)
# add super title and save full image
suptitle_font = {'family':'Century Gothic', 'fontsize':60, 'fontweight':'normal', 'y':1.07}
fig.suptitle('City Street Network Orientation', **suptitle_font)
fig.tight_layout()
fig.subplots_adjust(hspace=0.35)
fig.savefig('images/street-orientations.png', dpi=120, bbox_inches='tight')
plt.close()
|
_____no_output_____
|
MIT
|
notebooks/17-street-network-orientations.ipynb
|
baerbelblume/osmnx-examples
|
Modelado de Robots Recordando la práctica anterior, tenemos que la ecuación diferencial que caracteriza a un sistema masa-resorte-amoritguador es:$$m \ddot{x} + c \dot{x} + k x = F$$y revisamos 3 maneras de obtener el comportamiento de ese sistema, sin embargo nos interesa saber el comportamiento de un sistema mas complejo, un robot; empezaremos con un pendulo simple, el cual tiene la siguiente ecuación de movimiento:$$m l^2 \ddot{q} + m g l \cos{q} = \tau$$Como podemos ver, son similares en el sentido de que involucran una sola variable, sin embargo, en la segunda ecuación, nuestra variable esta involucrada adentro de una función no lineal ($\cos{q}$), por lo que nuestra ecuación diferencial es no lineal, y por lo tanto _no_ podemos usar el formalismo de función de transferencia para resolverla; tenemos que usar la función ```odeint``` para poder resolverla.Como es de segundo grado, tenemos que dividir nuestra ecuación diferencial en dos mas simples, por lo tanto usaremos el siguiente truco:$$\frac{d}{dt} q = \dot{q}$$entonces, tenemos dos ecuaciones diferenciales, por lo que podemos resolver dos incognitas $q$ y $\dot{q}$.Utilizando nuestros conocimientos de algebra lineal, podemos acomodar nuestro sistema de ecuaciones en una matriz, de tal manera que si antes teniamos que:$$\begin{align}\frac{d}{dt} q &= \dot{q} \\\frac{d}{dt} \dot{q} &= \ddot{q} = \frac{\tau - m g l \cos{q}}{ml^2}\end{align}$$Por lo que podemos ver que nuestro sistema de ecuaciones tiene un estado mas grande que antes; la ecuación diferencial que teniamos como no lineal, de segundo orden, podemos escribirla como no lineal, de primer orden siempre y cuando nuestro estado sea mas grande.Definamos a lo que nos referimos con estado:$$x =\begin{pmatrix}q \\\dot{q}\end{pmatrix}$$con esta definición de estado, podemos escribir el sistema de ecuaciónes de arriba como:$$\frac{d}{dt} x = \dot{x} = \frac{d}{dt}\begin{pmatrix}q \\\dot{q}\end{pmatrix} =\begin{pmatrix}\dot{q} \\\frac{\tau - m g l \cos{q}}{ml^2}\end{pmatrix}$$o bien $\dot{x} = f(x)$, en donde $f(x)$ es una función vectorial, o bien, un vector de funciones:$$f(x) =\begin{pmatrix}\dot{q} \\\frac{\tau - m g l \cos{q}}{ml^2}\end{pmatrix}$$Por lo que ya estamos listos para simular este sistema mecánico, con la ayuda de ```odeint()```; empecemos importando laas librerias necesarias:
|
from scipy.integrate import odeint
from numpy import linspace
|
_____no_output_____
|
MIT
|
Practicas/.ipynb_checkpoints/Practica 5 - Modelado de Robots-checkpoint.ipynb
|
robblack007/clase-dinamica-robot
|
y definiendo una función que devuelva un arreglo con los valores de $f(x)$
|
def f(x, t):
from numpy import cos
q, q̇ = x
τ = 0
m = 1
g = 9.81
l = 1
return [q̇, τ - m*g*l*cos(q)/(m*l**2)]
|
_____no_output_____
|
MIT
|
Practicas/.ipynb_checkpoints/Practica 5 - Modelado de Robots-checkpoint.ipynb
|
robblack007/clase-dinamica-robot
|
Vamos a simular desde el tiempo $0$, hasta $10$, y las condiciones iniciales del pendulo son $q=0$ y $\dot{q} = 0$.
|
ts = linspace(0, 10, 100)
x0 = [0, 0]
|
_____no_output_____
|
MIT
|
Practicas/.ipynb_checkpoints/Practica 5 - Modelado de Robots-checkpoint.ipynb
|
robblack007/clase-dinamica-robot
|
Utilizamos la función ```odeint``` para simular el comportamiento del pendulo, dandole la función que programamos con la dinámica de $f(x)$ y sacamos los valores de $q$ y $\dot{q}$ que nos devolvió ```odeint``` envueltos en el estado $x$
|
xs = odeint(func = f, y0 = x0, t = ts)
qs, q̇s = list(zip(*xs.tolist()))
|
_____no_output_____
|
MIT
|
Practicas/.ipynb_checkpoints/Practica 5 - Modelado de Robots-checkpoint.ipynb
|
robblack007/clase-dinamica-robot
|
En este punto ya tenemos nuestros datos de la simulación, tan solo queda graficarlos para interpretar los resultados:
|
%matplotlib inline
from matplotlib.pyplot import style, plot, figure
style.use("ggplot")
fig1 = figure(figsize = (8, 8))
ax1 = fig1.gca()
ax1.plot(xs);
fig2 = figure(figsize = (8, 8))
ax2 = fig2.gca()
ax2.plot(qs)
ax2.plot(q̇s);
|
_____no_output_____
|
MIT
|
Practicas/.ipynb_checkpoints/Practica 5 - Modelado de Robots-checkpoint.ipynb
|
robblack007/clase-dinamica-robot
|
Pero las gráficas de trayectoria son aburridas, recordemos que podemos hacer una animación con matplotlib:
|
from matplotlib import animation
from numpy import sin, cos, arange
# Se define el tamaño de la figura
fig = figure(figsize=(8, 8))
# Se define una sola grafica en la figura y se dan los limites de los ejes x y y
axi = fig.add_subplot(111, autoscale_on=False, xlim=(-1.5, 1.5), ylim=(-2, 1))
# Se utilizan graficas de linea para el eslabon del pendulo
linea, = axi.plot([], [], "-o", lw=2, color='gray')
def init():
# Esta funcion se ejecuta una sola vez y sirve para inicializar el sistema
linea.set_data([], [])
return linea
def animate(i):
# Esta funcion se ejecuta para cada cuadro del GIF
# Se obtienen las coordenadas x y y para el eslabon
xs, ys = [[0, cos(qs[i])], [0, sin(qs[i])]]
linea.set_data(xs, ys)
return linea
# Se hace la animacion dandole el nombre de la figura definida al principio, la funcion que
# se debe ejecutar para cada cuadro, el numero de cuadros que se debe de hacer, el periodo
# de cada cuadro y la funcion inicial
ani = animation.FuncAnimation(fig, animate, arange(1, len(qs)), interval=25,
blit=True, init_func=init)
# Se guarda el GIF en el archivo indicado
ani.save('./imagenes/pendulo-simple.gif', writer='imagemagick');
|
_____no_output_____
|
MIT
|
Practicas/.ipynb_checkpoints/Practica 5 - Modelado de Robots-checkpoint.ipynb
|
robblack007/clase-dinamica-robot
|
Load the data Without the known blooming bacteria (from American Gut paper)
|
ca.set_log_level('ERROR')
ratios=ca.read_amplicon('../lefse_ratios/ratios.biom','../studies/index.csv',
feature_metadata_file='../taxonomy/DB1-15_taxonomy_svs_numbers.tsv',normalize=None, min_reads=None)
ca.set_log_level('INFO')
ratios.sparse = False
ratios
np.sum(np.sum(ratios.data==0,axis=0)>30)
ratios.feature_metadata['keep']=(np.sum(ratios.data==0,axis=0)<=30)
ratios=ratios.filter_by_metadata('keep',[True],axis='f')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Fix taxonomy and filter chloroplast/mitochondria
|
ratios.feature_metadata['taxonomy'] = ratios.feature_metadata.Taxon
ratios.feature_metadata['taxonomy'].fillna('NA',inplace=True)
ratios = ratios.filter_by_taxonomy(['chloroplast','cyanobacteria','mitochondria'],negate=True)
disease_colors = {}
disease_colors = {xx: (0,0,0) for xx in ratios.sample_metadata.disease.unique()}
disease_colors.update({'HIV': (1.00,0.93,0.35),'Autism': (0.50,0.99,0.52),'Bipolar': (1.00, 0.63, 0.00),
'IBD_Crohn disease': (0.72,0.11,0.11),'IBD_Ulcerative Colitis': (0.043,1,0.97),
'IBD_Inflammtory bowel disease': (0.90,0.59,0.043),
'Diabetes T2': (0.47,0.53,0.80),
'Depression': (0.48,0.12,0.64),
'Obesity': (0.25,0.32,0.71),
'Parkinson': (0.29,0.08,0.55),
'Schizophrenia': (0.88,0.75,0.91),
'Gastroenteritis': (0.94,0.33,0.31),
'Heart diseases': (0.33,0.43,1.00),
'Irritable bowel syndrom': (0.90,0.45,0.45),
'Alzheimer': (0.83, 0.83, 0.83), 'Anorexia': (0.83, 0.83, 0.83), 'Cancer': (0.83, 0.83, 0.83), 'Autoimmun diseases': (0.83, 0.83, 0.83), 'C.difficile infection': (0.83, 0.83, 0.83),
'Cancer': (0.83, 0.83, 0.83), 'Chronic fatigue syndrome': (0.83, 0.83, 0.83), 'Diabetes T1': (0.83, 0.83, 0.83), 'Gout': (0.83, 0.83, 0.83),
'Hepatitis B': (0.83, 0.83, 0.83), 'Hepatitis C': (0.83, 0.83, 0.83), 'Hypertension': (0.83, 0.83, 0.83),
'Lupus': (0.83, 0.83, 0.83), 'Pancreatitis': (0.83, 0.83, 0.83), 'Psoriasis': (0.83, 0.83, 0.83), 'Rheumatoid arthritis': (0.83, 0.83, 0.83),
})
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
creat a chart pie for diseases
|
ratios.sample_metadata['pie_disease']=ratios.sample_metadata.disease.copy()
ratios.sample_metadata.pie_disease.replace('Gout','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Irritable bowel syndrom','IBS',inplace=True)
ratios.sample_metadata.pie_disease.replace('Hepatitis B','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('IBD_Crohn disease','IBD',inplace=True)
ratios.sample_metadata.pie_disease.replace('IBD_Ulcerative Colitis','IBD',inplace=True)
ratios.sample_metadata.pie_disease.replace('IBD_Inflammtory bowel disease','IBD',inplace=True)
ratios.sample_metadata.pie_disease.replace('Alzheimer','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Anorexia','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Autoimmun diseases','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Cancer','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('C.difficile infection','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Diabetes T1','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Hypertension','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Chronic fatigue syndrome','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Gout','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('C.difficile infection','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Gout','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Lupus','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Pancreatitis','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Psoriasis','Other',inplace=True)
ratios.sample_metadata.pie_disease.replace('Rheumatoid arthritis','Other',inplace=True)
pass
disease_colors.update({'HIV': (1.00,0.93,0.35),'Autism': (0.50,0.99,0.52),
'Bipolar': (1.00, 0.63, 0.00),
'IBD': (0.72,0.11,0.11),
'Diabetes T2': (0.47,0.53,0.80),
'Depression': (0.48,0.12,0.64),
'Obesity': (0.25,0.32,0.71),
'Parkinson’s': (0.29,0.08,0.55),
'Schizophrenia': (0.88,0.75,0.91),
'Gastroenteritis': (0.94,0.33,0.31),
'Heart diseases': (0.33,0.43,1.00),
'IBS': (0.90,0.45,0.45),
'Other': (0.83, 0.83, 0.83)})
plt.figure()
pp=plt.pie(ratios.sample_metadata.pie_disease.value_counts(),textprops={'fontsize': 7}, labels=ratios.sample_metadata.pie_disease.unique(), labeldistance=0.5, rotatelabels=True)
for pie_wedge in pp[0]:
pie_wedge.set_edgecolor('white')
pie_wedge.set_facecolor(disease_colors[pie_wedge.get_label()])
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Prepare the colormap for the heatmapsWe want coolwarm, with white for exact 0s (which mean not present)
|
current_cmap = mpl.cm.get_cmap('coolwarm')
current_cmap.set_bad(color='red')
ncm = current_cmap(np.linspace(0,1,1000000))
ncm[500000]=(1,1,1,1)
ncm=mpl.colors.ListedColormap(ncm)
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Look at the data
|
ratios.feature_metadata
ratios.plot(gui='cli',norm=None,cmap=ncm ,clim=[-0.5,0.5], bad_color='w')
ratios.plot(gui='cli',norm=None,cmap=ncm ,clim=[-1,1], bad_color='w')
ratios=ratios.sort_abundance(key=np.mean)
ratios.plot(gui='cli',norm=None,cmap=ncm ,clim=[-1,1], bad_color='w')
# cu.splot(ratios,'disease',norm=None,cmap=ncm,clim=[-0.5,0.5],xticks_max=None)
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Plot all bacteria aggregate all samples by disease so CD/UC count as 1
|
ratios_agg=ratios.aggregate_by_metadata('disease',agg='mean')
ratios_agg
# cu.splot(ratios_agg,'disease',norm=None,cmap=ncm,clim=[-0.25,0.25],xticks_max=None)
ratios_agg.plot(sample_field='disease',norm=None,cmap=ncm,clim=[-0.25,0.25],xticks_max=None)
ratios
np.sum(ratios_agg.data[:]>0)
np.sum(ratios_agg.data[:]<0)
np.sum(ratios_agg.data[:]==0)
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Sort by mean abundance over all diseaseWith 1 sample per disease (aggregation by mean)
|
ratios_agg=ratios_agg.sort_abundance(key=np.mean)
# cu.splot(ratios_agg,'disease',norm=None,cmap=ncm,clim=[-0.25,0.25],xticks_max=None)
allbact = ratios.filter_ids(ratios_agg.feature_metadata.index)
allbact = allbact.sort_samples('disease')
allbact
f=allbact.plot(sample_field='disease',norm=None,cmap=ncm,clim=[-1,1],xticks_max=None,xticklabel_len=None,
xticklabel_kwargs={'size':8, 'rotation':90}, barx_fields=['disease'],barx_label=False,barx_colors=disease_colors)
f.save_figure('../figures/sup-heatmap-allbact-lefse.pdf')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Plot the non-specific bacteriaUsing the binomial sign test (only on experiments where the bacteria is present), with at least 4 experiments per bacteria. FDR=0.1The test is done on 1 aggregated sample per disease to prevent bias by disease with many studies
|
np.random.seed(2020)
nonspecific_agg=cu.get_sign_pvals(ratios_agg,alpha=0.25,min_present=4)
nonspecific = ratios.filter_ids(nonspecific_agg.feature_metadata.index)
nonspecific = nonspecific.sort_samples('disease')
nonspecific.feature_metadata = nonspecific.feature_metadata.join(nonspecific_agg.feature_metadata,lsuffix='',rsuffix='_agg')
nonspecific.plot(sample_field='disease',norm=None,cmap=ncm,clim=[-0.5,0.5],xticks_max=None,xticklabel_len=None)
cu.splot(nonspecific,'disease',norm=None,cmap=ncm,clim=[-1,1],xticks_max=None,xticklabel_len=None)
f=nonspecific.plot(sample_field='disease',norm=None,cmap=ncm,clim=[-1,1],xticks_max=None,xticklabel_len=None,
xticklabel_kwargs={'size':8, 'rotation':90},barx_fields=['disease'],barx_label=False,barx_colors=disease_colors)
f.save_figure('../figures/sup-heatmap-nonspecific-lefse.pdf')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Save the non-secific bacteria
|
nonspecific_agg.save('../lefse_ratios/nonspecific/nonspecific')
nonspecific_agg.save_fasta('../lefse_ratios/nonspecific/nonspecific.fa',header='seq')
nonspecific.save('../lefse_ratios/nonspecific/nonspecific_all',fmt='txt')
|
2022-01-05 19:09:04 WARNING .txt format does not support taxonomy information in save. Saving without taxonomy.
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Also save only the ones going up or down
|
nsup_ids=nonspecific_agg.feature_metadata[nonspecific_agg.feature_metadata.esize > 0]
nsdown_ids=nonspecific_agg.feature_metadata[nonspecific_agg.feature_metadata.esize < 0]
len(nsup_ids)
len(nsdown_ids)
nsup = nonspecific.filter_ids(nsup_ids.index)
nsup.save('../lefse_ratios/nonspecific/nonspecific-up')
nsdown = nonspecific.filter_ids(nsdown_ids.index)
nsdown.save('../lefse_ratios/nonspecific/nonspecific-down')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
how many higher/lower in non-specific
|
np.sum(nonspecific_agg.feature_metadata.esize<0)
np.sum(nonspecific_agg.feature_metadata.esize>0)
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Get the enriched dbBact terms
|
nonspecific_agg.feature_metadata['_calour_stat'] = nonspecific_agg.feature_metadata['esize']
nonspecific_agg.feature_metadata['_calour_direction'] = 'down'
nonspecific_agg.feature_metadata.loc[nonspecific_agg.feature_metadata['esize']>0,'_calour_direction']='up'
f,dterms = nonspecific_agg.plot_diff_abundance_enrichment()
f.figure.savefig('../figures/sup-nonspecific-dbbact-terms-lefse.pdf')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Draw the dbbact term wordcloud for the non-specific bacteria
|
dbbact=ca.database._get_database_class('dbbact')
f=dbbact.draw_wordcloud(nonspecific)
f.savefig('../figures/sup-wordcloud-nonspecific-lefse.pdf')
f=dbbact.draw_wordcloud(nsup)
f.savefig('../figures/sup-wordcloud-nonspecific-up-lefse.pdf')
f=dbbact.draw_wordcloud(nsdown)
f.savefig('../figures/sup-wordcloud-nonspecific-down-lefse.pdf')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
IBD specific
|
def nzdiff(data,labels):
'''Calculate the mean difference between two groups without using 0s
used for the calour.diff_abundance for only non-zero samples
Parameters
----------
data: np.array
sample * feature(similar to calour Experiment.data)
labels:::: np.array of 0s and 1s
the label for each sample.
Returns
-------
np.array
for each feature, mean(group1:group1!=0)- mean(group2: group2!=0)
'''
data0=data[:,labels==0]
data1=data[:,labels==1]
res = np.zeros(data.shape[0])
for i in range(data.shape[0]):
m1=data1[i,:]
m1=m1[m1!=0]
if len(m1) == 0:
continue
m1=np.mean(m1)
m0=data0[i,:]
m0=m0[m0!=0]
if len(m0) == 0:
continue
m0=np.mean(m0)
res[i]= m1 - m0
return res
def ratio_enrichment(exp, field, val1, val2=None, alpha=0.1, min_prev=3, random_seed=None, transform=None):
'''Identify bacteria significantly enriched (i.e. ratios higher/lower) in samples with field=val1 vs. val2 (or all other samples if val2==None)
Test is performed only on non-zero features present in at least min_prev samples in each group.
Parameters
----------
exp: calour.Experiment
The experiment to test
field: str
Name of the field for identifying the 2 groups of samples
val1: str or list of str
Values of field for the first group of samples
val2: str or list of str or None
Values of field for the second group of samples. If None, use all samples not with val1
alpha: float, optional
the dsFDR threshold
min_prev: int, optional
use only bacteria present in at least min_prev samples (not 0) in each group
random_seed: int, optional
transform: str or None, optional
the data transform (from ca.diff_abundance)
'''
# pre filter the data to keep only features present in enough samples in both groups
e1 = exp.filter_samples(field, val1)
e1.sparse=False
e1.data[e1.data!=0] = 1
e1 = e1.filter_sum_abundance(min_prev)
if val2 is None:
e2 = exp.filter_samples(field, val1, negate=True)
else:
e2 = exp.filter_samples(field, val2)
e2.sparse=False
e2.data[e2.data!=0] = 1
e2 = e2.filter_sum_abundance(min_prev)
# keep only features present in > min_prev samples in group1 and group2
exp = exp.filter_ids(e1.feature_metadata.index)
exp = exp.filter_ids(e2.feature_metadata.index)
print('%d remaining after filtering for min_prev %d' % (len(exp.feature_metadata), min_prev))
# find the features significantly different between group1 and group2
# we use the nzdiff statist
dd=exp.diff_abundance(field,val1,val2, transform=transform,alpha=alpha,method=nzdiff,random_seed=random_seed)
return dd
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
remove the biopsies studies
|
ratios_no_biop = ratios.filter_samples('_sample_id',['23', '29', '49', '52'],negate=True)
ratios_no_biop
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Calculate the specific bacteria without the Gevers biopsies studies
|
def nice_taxonomy(exp):
'''add nice taxonomy string (only phyla+genus+species if available) for heatmap
Parameters
----------
exp: calour.AmpliconExperiment
with the taxonomy in 'Taxon' field
Returns
-------
exp: calour.AmpliconExperiment, with added feature metadata field "nice_tax"
'''
nice_tax=[]
for cidx,crow in exp.feature_metadata.iterrows():
ctax = crow['Taxon']
ctax=ctax.split(';')
new_tax = ctax[1].split('_')[-1]+'|'
if len(ctax) > 5:
new_tax += ctax[5].split('_')[-1]
if len(ctax) > 6:
if len(ctax[6])>4:
new_tax += '|'+ctax[6].split('_')[-1]
else:
new_tax += ctax[-1].split('_')[-1]
nice_tax.append(new_tax)
newexp = exp.copy()
newexp.feature_metadata['nice_tax'] = nice_tax
return newexp
np.random.seed(2020)
specific_no_biop=ratio_enrichment(ratios_no_biop, 'disease',['IBD_Crohn disease','IBD_Ulcerative Colitis'],
alpha=0.1, min_prev=3,random_seed=2020, transform='rankdata')
specific_no_biop.save('../lefse_ratios/ibd_specific/ibd-no-biopsies-specific')
specific_no_biop.save_fasta('../lefse_ratios/ibd_specific/ibd-no-biopsies-specific')
specific_no_biop = specific_no_biop.sort_samples('disease')
specific_no_biop = nice_taxonomy(specific_no_biop)
f=specific_no_biop.plot(sample_field='disease',norm=None,cmap=ncm,clim=[-1,1],
xticks_max=None,xticklabel_len=None, xticklabel_kwargs={'size':5, 'rotation':90},
feature_field='nice_tax', yticklabel_len=None, yticklabel_kwargs={'size':5}, barx_fields=['disease'],barx_label=False,barx_colors=disease_colors)
f.figure.savefig('../figures/sup-heatmap-specific-lefse.pdf')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
draw the wordcloud for the CD/UC specific bacteria
|
f=dbbact.draw_wordcloud(specific_no_biop)
f.savefig('../figures/sup-wordcloud-specific-lefse.pdf')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Venn comparison to main analysis
|
import matplotlib_venn
ns_norarefaction_down = pd.read_csv('../ratios/nonspecific/nonspecific-down_feature.txt',sep='\t')
ns_lefse_down = pd.read_csv('../lefse_ratios/nonspecific/nonspecific-down_feature.txt',sep='\t')
ns_norarefaction_up = pd.read_csv('../ratios/nonspecific/nonspecific-up_feature.txt',sep='\t')
ns_lefse_up = pd.read_csv('../lefse_ratios/nonspecific/nonspecific-up_feature.txt',sep='\t')
f=plt.figure()
matplotlib_venn.venn3([set(ns_norarefaction_up['_feature_id'].values),set(ns_lefse_up['_feature_id'].values),set(ns_lefse_down['_feature_id'].values)],set_labels=['NR up','LEFSE up','LEFSE down'])
f.savefig('../figures/sup-fig-venn-lefse-up.pdf')
f=plt.figure()
matplotlib_venn.venn3([set(ns_norarefaction_down['_feature_id'].values),set(ns_lefse_up['_feature_id'].values),set(ns_lefse_down['_feature_id'].values)],set_labels=['NR up','LEFSE up','LEFSE down'])
f.savefig('../figures/sup-fig-venn-lefse-down.pdf')
spec_norarefaction = pd.read_csv('../ratios/ibd_specific/ibd-no-biopsies-specific_feature.txt',sep='\t')
spec_lefse = pd.read_csv('../lefse_ratios/ibd_specific/ibd-no-biopsies-specific_feature.txt',sep='\t')
f=plt.figure()
matplotlib_venn.venn2([set(spec_norarefaction['_feature_id'].values),set(spec_lefse['_feature_id'].values)],set_labels=['NRMS','LEFSE'])
# f.savefig('../figures/sup-fig-venn-lefse-down.pdf')
ib=set(spec_norarefaction['_feature_id'].values).intersection(set(spec_lefse['_feature_id'].values))
print([spec_norarefaction[spec_norarefaction['_feature_id']==x]['SV_number'].values for x in ib])
spec_norarefaction.iloc[0]['Taxon']
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
compare lefse to nrmd using all lefse features and direction of change
|
nrmd_up=pd.read_csv('../ratios/nonspecific/nonspecific-up_feature.txt',sep='\t',index_col=0)
nrmd_down=pd.read_csv('../ratios/nonspecific/nonspecific-down_feature.txt',sep='\t',index_col=0)
all_lefse = pd.read_csv('../lefse_ratios/all_lefse_ratios.txt',sep='\t',index_col=0)
up_dir=all_lefse.filter(nrmd_up.index,axis='index')
down_dir=all_lefse.filter(nrmd_down.index,axis='index')
print('in NRMD up (%d), %d (LEFSE>0), %d (LEFSE<0)'% (len(nrmd_up),np.sum(np.mean(up_dir, axis=1)>0),np.sum(np.mean(up_dir, axis=1)<0)))
print('in NRMD down (%d), %d (LEFSE>0), %d (LEFSE<0)'% (len(nrmd_down),np.sum(np.mean(down_dir, axis=1)>0),np.sum(np.mean(down_dir, axis=1)<0)))
smd=pd.read_csv('../studies/index.csv',sep='\t',index_col=0)
smd.index=smd.index.astype(str)
xx=ca.AmpliconExperiment.from_pandas(up_dir.transpose())
xx.sample_metadata=xx.sample_metadata.merge(smd,how='left',left_index=True,right_index=True)
xx=xx.sort_samples('disease')
f=xx.plot(sample_field='disease', clim=[-1,1],norm=None,cmap=ncm, xticks_max=None,xticklabel_len=None,
xticklabel_kwargs={'size':8, 'rotation':90},barx_fields=['disease'],barx_label=False,barx_colors=disease_colors,bad_color='w')
f.save_figure('../figures/sup-lefse-dir-for-nrmd-up.pdf')
xx=ca.AmpliconExperiment.from_pandas(down_dir.transpose())
xx.sample_metadata=xx.sample_metadata.merge(smd,how='left',left_index=True,right_index=True)
xx=xx.sort_samples('disease')
f=xx.plot(sample_field='disease', clim=[-1,1],norm=None,cmap=ncm, xticks_max=None,xticklabel_len=None,
xticklabel_kwargs={'size':8, 'rotation':90},barx_fields=['disease'],barx_label=False,barx_colors=disease_colors, bad_color='w')
f.save_figure('../figures/sup-lefse-dir-for-nrmd-down.pdf')
|
_____no_output_____
|
MIT
|
scripts/ratios-lefse.ipynb
|
amnona/paper-metaanalysis
|
Use BlackJAX with Numpyro BlackJAX can take any log-probability function as long as it is compatible with JAX's JIT. In this notebook we show how we can use Numpyro as a modeling language and BlackJAX as an inference library.We reproduce the Eight Schools example from the [Numpyro documentation](https://github.com/pyro-ppl/numpyro) (all credit for the model goes to the Numpyro team). For this notebook to run you will need to install Numpyro:```bashpip install numpyro```
|
import jax
import numpy as np
import numpyro
import numpyro.distributions as dist
from numpyro.infer.reparam import TransformReparam
from numpyro.infer.util import initialize_model
import blackjax
num_warmup = 1000
# We can use this notebook for simple benchmarking by setting
# below to True and run from Terminal.
# $ipython examples/use_with_numpyro.ipynb
RUN_BENCHMARK = False
if RUN_BENCHMARK:
num_sample = 5_000_000
print(f"Benchmark with {num_warmup} warmup steps and {num_sample} sampling steps.")
else:
num_sample = 10_000
|
_____no_output_____
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
Data
|
# Data of the Eight Schools Model
J = 8
y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
|
_____no_output_____
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
Model We use the non-centered version of the model described towards the end of the README on Numpyro's repository:
|
# Eight Schools example - Non-centered Reparametrization
def eight_schools_noncentered(J, sigma, y=None):
mu = numpyro.sample("mu", dist.Normal(0, 5))
tau = numpyro.sample("tau", dist.HalfCauchy(5))
with numpyro.plate("J", J):
with numpyro.handlers.reparam(config={"theta": TransformReparam()}):
theta = numpyro.sample(
"theta",
dist.TransformedDistribution(
dist.Normal(0.0, 1.0), dist.transforms.AffineTransform(mu, tau)
),
)
numpyro.sample("obs", dist.Normal(theta, sigma), obs=y)
|
_____no_output_____
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
We need to translate the model into a log-probability function that will be used by BlackJAX to perform inference. For that we use the `initialize_model` function in Numpyro's internals. We will also use the initial position it returns:
|
rng_key = jax.random.PRNGKey(0)
init_params, potential_fn_gen, *_ = initialize_model(
rng_key,
eight_schools_noncentered,
model_args=(J, sigma, y),
dynamic_args=True,
)
|
_____no_output_____
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
Now we create the potential using the `potential_fn_gen` provided by Numpyro and initialize the NUTS state with BlackJAX:
|
if RUN_BENCHMARK:
print("\nBlackjax:")
print("-> Running warmup.")
|
_____no_output_____
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
We now run the window adaptation in BlackJAX:
|
%%time
initial_position = init_params.z
logprob = lambda position: -potential_fn_gen(J, sigma, y)(position)
adapt = blackjax.window_adaptation(
blackjax.nuts, logprob, num_warmup, target_acceptance_rate=0.8
)
last_state, kernel, _ = adapt.run(rng_key, initial_position)
|
CPU times: user 2.43 s, sys: 7.96 ms, total: 2.44 s
Wall time: 2.42 s
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
Let us now perform inference using the previously computed step size and inverse mass matrix. We also time the sampling to give you an idea of how fast BlackJAX can be on simple models:
|
if RUN_BENCHMARK:
print("-> Running sampling.")
%%time
def inference_loop(rng_key, kernel, initial_state, num_samples):
@jax.jit
def one_step(state, rng_key):
state, info = kernel(rng_key, state)
return state, (state, info)
keys = jax.random.split(rng_key, num_samples)
_, (states, infos) = jax.lax.scan(one_step, initial_state, keys)
return states, (
infos.acceptance_probability,
infos.is_divergent,
infos.integration_steps,
)
# Sample from the posterior distribution
states, infos = inference_loop(rng_key, kernel, last_state, num_sample)
_ = states.position["mu"].block_until_ready()
|
CPU times: user 2.25 s, sys: 30.2 ms, total: 2.28 s
Wall time: 2.26 s
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
Let us compute the average acceptance probability and check the number of divergences (to make sure that the model sampled correctly, and that the sampling time is not a result of a majority of divergent transitions):
|
acceptance_rate = np.mean(infos[0])
num_divergent = np.mean(infos[1])
print(f"\nAcceptance rate: {acceptance_rate:.2f}")
print(f"{100*num_divergent:.2f}% divergent transitions")
|
Acceptance rate: 0.89
0.02% divergent transitions
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
Let us now plot the distribution of the parameters. Note that since we use a transformed variable, Numpyro does not output the school treatment effect directly:
|
if not RUN_BENCHMARK:
import seaborn as sns
from matplotlib import pyplot as plt
samples = states.position
fig, axes = plt.subplots(ncols=2)
fig.set_size_inches(12, 5)
sns.kdeplot(samples["mu"], ax=axes[0])
sns.kdeplot(samples["tau"], ax=axes[1])
axes[0].set_xlabel("mu")
axes[1].set_xlabel("tau")
fig.tight_layout()
if not RUN_BENCHMARK:
fig, axes = plt.subplots(8, 2, sharex="col", sharey="col")
fig.set_size_inches(12, 10)
for i in range(J):
axes[i][0].plot(samples["theta_base"][:, i])
axes[i][0].title.set_text(f"School {i} relative treatment effect chain")
sns.kdeplot(samples["theta_base"][:, i], ax=axes[i][1], shade=True)
axes[i][1].title.set_text(f"School {i} relative treatment effect distribution")
axes[J - 1][0].set_xlabel("Iteration")
axes[J - 1][1].set_xlabel("School effect")
fig.tight_layout()
plt.show()
if not RUN_BENCHMARK:
for i in range(J):
print(
f"Relative treatment effect for school {i}: {np.mean(samples['theta_base'][:, i]):.2f}"
)
|
Relative treatment effect for school 0: 0.34
Relative treatment effect for school 1: 0.11
Relative treatment effect for school 2: -0.09
Relative treatment effect for school 3: 0.07
Relative treatment effect for school 4: -0.16
Relative treatment effect for school 5: -0.07
Relative treatment effect for school 6: 0.35
Relative treatment effect for school 7: 0.07
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
Compare sampling time with NumpyroWe compare the time it took BlackJAX to do the warmup for 1,000 iterations and then taking 100,000 samples with Numpyro's:
|
from numpyro.infer import MCMC, NUTS
if RUN_BENCHMARK:
print("\nNumpyro:")
print("-> Running warmup+sampling.")
%%time
nuts_kernel = NUTS(eight_schools_noncentered, target_accept_prob=0.8)
mcmc = MCMC(
nuts_kernel, num_warmup=num_warmup, num_samples=num_sample, progress_bar=False
)
rng_key = jax.random.PRNGKey(0)
mcmc.run(rng_key, J, sigma, y=y, extra_fields=("num_steps", "accept_prob"))
samples = mcmc.get_samples()
_ = samples["mu"].block_until_ready()
print(f"\nAcceptance rate: {mcmc.get_extra_fields()['accept_prob'].mean():.2f}")
print(f"{100*mcmc.get_extra_fields()['diverging'].mean():.2f}% divergent transitions")
print(f"\nBlackjax average {infos[2].mean():.2f} leapfrog per iteration.")
print(
f"Numpyro average {mcmc.get_extra_fields()['num_steps'].mean():.2f} leapfrog per iteration."
)
|
Blackjax average 7.11 leapfrog per iteration.
Numpyro average 8.91 leapfrog per iteration.
|
Apache-2.0
|
examples/use_with_numpyro.ipynb
|
hriebl/blackjax
|
The visualization used for this homework is based on Alexandr Verinov's code. Generative models In this homework we will try several criterions for learning an implicit model. Almost everything is written for you, and you only need to implement the objective for the game and play around with the model. **0)** Read the code**1)** Implement objective for a vanilla [Generative Adversarial Networks](https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf) (GAN). The hyperparameters are already set in the code. The model will converge if you implement the objective (1) right. **2)** Note the discussion in the paper, that the objective for $G$ can be of two kinds: $min_G log(1 - D)$ and $min_G - log(D)$. Implement the second objective and ensure model converges. Most likely, in this example you will not notice the difference, but people usually use the second objective, it really matters in more complicated scenarios.**3 & 4)** Implement [Wasserstein GAN](https://arxiv.org/abs/1701.07875) ([WGAN](https://arxiv.org/abs/1704.00028)) and WGAN-GP. To make the discriminator have Lipschitz property you need to clip discriminator's weights to $[-0.01, 0.01]$ range (WGAN) or use gradient penalty (WGAN-GP). You will need to make few modifications to the code: 1) remove sigmoids from discriminator 2) add weight clipping clipping / gradient penaly. 3) change objective. See [implementation 1](https://github.com/martinarjovsky/WassersteinGAN/) / [implementation 2](https://github.com/caogang/wgan-gp). They also use different optimizer. The default hyperparameters may not work, spend time to tune them.**5) Bonus: same thing without GANs** Implement maximum mean discrepancy estimator (MMD). MMD is discrepancy measure between distributions. In our case we use it to calculate discrepancy between real and fake data. You need to implement RBF kernel $k(x,x')=\exp \left(-{\frac {1}{2\sigma ^{2}}}||x-x'||^{2}\right)$ and an MMD estimator (see eq.8 from https://arxiv.org/pdf/1505.03906.pdf). MMD is then used instead of discriminator.
|
#!L
"""
Please, implement everything in one notebook, using if statements to switch between the tasks
"""
TASK = 1 # 2, 3, 4, 5
|
_____no_output_____
|
MIT
|
homework03/homework03_part3_gan_basic.ipynb
|
VendettaPrime/Practical_DL
|
Imports
|
#!L
import numpy as np
import time
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(12345)
lims=(-5, 5)
|
_____no_output_____
|
MIT
|
homework03/homework03_part3_gan_basic.ipynb
|
VendettaPrime/Practical_DL
|
Define sampler from real data and Z
|
#!L
from scipy.stats import rv_discrete
MEANS = np.array(
[[-1,-3],
[1,3],
[-2,0],
])
COVS = np.array(
[[[1,0.8],[0.8,1]],
[[1,-0.5],[-0.5,1]],
[[1,0],[0,1]],
])
PROBS = np.array([
0.2,
0.5,
0.3
])
assert len(MEANS) == len(COVS) == len(PROBS), "number of components mismatch"
COMPONENTS = len(MEANS)
comps_dist = rv_discrete(values=(range(COMPONENTS), PROBS))
def sample_true(N):
comps = comps_dist.rvs(size=N)
conds = np.arange(COMPONENTS)[:,None] == comps[None,:]
arr = np.array([np.random.multivariate_normal(MEANS[c], COVS[c], size=N)
for c in range(COMPONENTS)])
return np.select(conds[:,:,None], arr).astype(np.float32)
NOISE_DIM = 20
def sample_noise(N):
return np.random.normal(size=(N,NOISE_DIM)).astype(np.float32)
|
_____no_output_____
|
MIT
|
homework03/homework03_part3_gan_basic.ipynb
|
VendettaPrime/Practical_DL
|
Visualization functions
|
#!L
def vis_data(data):
"""
Visualizes data as histogram
"""
hist = np.histogram2d(data[:, 1], data[:, 0], bins=100, range=[lims, lims])
plt.pcolormesh(hist[1], hist[2], hist[0], alpha=0.5)
fixed_noise = sample_noise(1000)
def vis_g():
"""
Visualizes generator's samples as circles
"""
data = generator(Variable(torch.Tensor(fixed_noise))).data.numpy()
if np.isnan(data).any():
return
plt.scatter(data[:,0], data[:,1], alpha=0.2, c='b')
plt.xlim(lims)
plt.ylim(lims)
def vis_d():
"""
Visualizes discriminator's gradient on grid
"""
X, Y = np.meshgrid(np.linspace(lims[0], lims[1], 30), np.linspace(lims[0], lims[1], 30))
X = X.flatten()
Y = Y.flatten()
grid = Variable(torch.Tensor(np.vstack([X, Y]).T), requires_grad=True)
data_gen = generator(Variable(torch.Tensor(fixed_noise)))
loss = d_loss(discriminator(data_gen), discriminator(grid))
loss.backward()
grads = - grid.grad.data.numpy()
plt.quiver(X, Y, grads[:, 0], grads[:, 1], color='black',alpha=0.9)
|
_____no_output_____
|
MIT
|
homework03/homework03_part3_gan_basic.ipynb
|
VendettaPrime/Practical_DL
|
Define architectures After you've passed task 1 you can play with architectures. Generator
|
#!L
class Generator(nn.Module):
def __init__(self, noise_dim, out_dim, hidden_dim=100):
super(Generator, self).__init__()
self.fc1 = nn.Linear(noise_dim, hidden_dim)
nn.init.xavier_normal_(self.fc1.weight)
nn.init.constant_(self.fc1.bias, 0.0)
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
nn.init.xavier_normal_(self.fc2.weight)
nn.init.constant_(self.fc2.bias, 0.0)
self.fc3 = nn.Linear(hidden_dim, out_dim)
nn.init.xavier_normal_(self.fc3.weight)
nn.init.constant_(self.fc3.bias, 0.0)
def forward(self, z):
"""
Generator takes a vector of noise and produces sample
"""
h1 = F.tanh(self.fc1(z))
h2 = F.leaky_relu(self.fc2(h1))
y_gen = self.fc3(h2)
return y_gen
|
_____no_output_____
|
MIT
|
homework03/homework03_part3_gan_basic.ipynb
|
VendettaPrime/Practical_DL
|
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