markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
Squelching Line OutputYou might have noticed the annoying line of the form `[]` before the plots. This is because the `.plot` function actually produces output. Sometimes we wish not to display output, we can accomplish this with the semi-colon as follows. | plt.plot(X); | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Adding Axis LabelsNo self-respecting quant leaves a graph without labeled axes. Here are some commands to help with that. | X = np.random.normal(0, 1, 100)
X2 = np.random.normal(0, 1, 100)
plt.plot(X);
plt.plot(X2);
plt.xlabel('Time') # The data we generated is unitless, but don't forget units in general.
plt.ylabel('Returns')
plt.legend(['X', 'X2']); | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Generating StatisticsLet's use `numpy` to take some simple statistics. | Y = np.mean(X)
Y
Y = np.std(X)
Y | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Getting Real Pricing DataRandomly sampled data can be great for testing ideas, but let's get some real data. We can use `get_pricing` to do that. You can use the `?` syntax as discussed above to get more information on `get_pricing`'s arguments. | !pip install yfinance
!pip install yahoofinancials
import yfinance as yf
from yahoofinancials import YahooFinancials
# Reference: https://towardsdatascience.com/a-comprehensive-guide-to-downloading-stock-prices-in-python-2cd93ff821d4
data = yf.download('MSFT', start='2012-01-01', end='2015-06-01', progress=False) | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Our data is now a dataframe. You can see the datetime index and the colums with different pricing data. | data | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
This is a pandas dataframe, so we can index in to just get price like this. For more info on pandas, please [click here](http://pandas.pydata.org/pandas-docs/stable/10min.html). | X = data['Open']
X | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Because there is now also date information in our data, we provide two series to `.plot`. `X.index` gives us the datetime index, and `X.values` gives us the pricing values. These are used as the X and Y coordinates to make a graph. | plt.plot(X.index, X.values)
plt.ylabel('Price')
plt.legend(['MSFT']);
np.mean(X)
np.std(X) | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Getting Returns from PricesWe can use the `pct_change` function to get returns. Notice how we drop the first element after doing this, as it will be `NaN` (nothing -> something results in a NaN percent change). | R = X.pct_change()[1:] | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
We can plot the returns distribution as a histogram. | plt.hist(R, bins=20)
plt.xlabel('Return')
plt.ylabel('Frequency')
plt.legend(['MSFT Returns']); | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Get statistics again. | np.mean(R)
np.std(R) | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Now let's go backwards and generate data out of a normal distribution using the statistics we estimated from Microsoft's returns. We'll see that we have good reason to suspect Microsoft's returns may not be normal, as the resulting normal distribution looks far different. | plt.hist(np.random.normal(np.mean(R), np.std(R), 10000), bins=20)
plt.xlabel('Return')
plt.ylabel('Frequency')
plt.legend(['Normally Distributed Returns']); | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
Generating a Moving Average`pandas` has some nice tools to allow us to generate rolling statistics. Here's an example. Notice how there's no moving average for the first 60 days, as we don't have 60 days of data on which to generate the statistic. | # Take the average of the last 60 days at each timepoint.
MAVG = X.rolling(60)
plt.plot(X.index, X.values)
plt.ylabel('Price')
plt.legend(['MSFT', '60-day MAVG']); | _____no_output_____ | MIT | Lab01/lmbaeza-lecture1.ipynb | lmbaeza/numerical-methods-2021 |
#@markdown Before starting please save the notebook in your drive by clicking on `File -> Save a copy in drive`
#@markdown Check how many CPUs we have, you can choose a high memory instance to get 4.
import os
print(f"We have {os.cpu_count()} CPU cores.")
#@markdown Mount google drive
from google.colab import drive, ou... | _____no_output_____ | MIT | Resample_Audio.ipynb | materialvision/melgan-neurips | |
Talktorial 5 Compound clustering Developed in the CADD seminars 2017 and 2018, AG Volkamer, Charité/FU Berlin Calvinna Caswara and Gizem Spriewald Aim of this talktorialSimilar compounds might bind to the same targets and show similar effects. Based on this similar property principle, compound similarity can be used ... | from IPython.display import IFrame
IFrame('images/butina_full.pdf', width=600, height=300) | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
*Figure 1:* Theoretical example of the Butina clustering algorithm, drawn by Calvinna Caswara. Picking diverse compoundsFinding representative sets of compounds is a concept often used in pharmaceutical industry.* Let's say, we applied a virtual screening campaign but only have a limited amount of resources to experim... | # Import packages
import pandas as pd
import numpy
import matplotlib.pyplot as plt
import time
import random
from random import choices
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit import DataStructs
from rdkit.DataStructs import cDataStructs
from rdkit.ML.Cluster import Butina
from rdkit.Chem impor... | Number of compounds converted: 4925
Fingerprint length per compound: 2048
| CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
2. Tanimoto similarity and distance matrixNow that we generated fingerprints, we move on to the next step: The identification of potential cluster centroids. For this, we define functions to calculate the Tanimoto similarity and distance matrix. | # Calculate distance matrix for fingerprint list
def Tanimoto_distance_matrix(fp_list):
dissimilarity_matrix = []
for i in range(1,len(fp_list)):
similarities = DataStructs.BulkTanimotoSimilarity(fp_list[i],fp_list[:i])
# Since we need a distance matrix, calculate 1-x for every element in simila... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
See also [rdkit Cookbook: Clustering molecules](http://rdkit.org/docs/Cookbook.htmlclustering-molecules). | # Example: Calculate single similarity of two fingerprints
sim = DataStructs.TanimotoSimilarity(fingerprints[0],fingerprints[1])
print ('Tanimoto similarity: %4.2f, distance: %4.2f' %(sim,1-sim))
# Example: Calculate distance matrix (distance = 1-similarity)
Tanimoto_distance_matrix(fingerprints)[0:5]
# Side note: That... | 12125350 12125350
| CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
3. Clustering molecules: Centroids and exclusion spheresIn this part, we cluster the molecules and look at the results. Define a clustering function. | # Input: Fingerprints and a threshold for the clustering
def ClusterFps(fps,cutoff=0.2):
# Calculate Tanimoto distance matrix
distance_matr = Tanimoto_distance_matrix(fps)
# Now cluster the data with the implemented Butina algorithm:
clusters = Butina.ClusterData(distance_matr,len(fps),cutoff,isDistData... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Cluster the molecules based on their fingerprint similarity. | # Run the clustering procedure for the dataset
clusters = ClusterFps(fingerprints,cutoff=0.3)
# Give a short report about the numbers of clusters and their sizes
num_clust_g1 = len([c for c in clusters if len(c) == 1])
num_clust_g5 = len([c for c in clusters if len(c) > 5])
num_clust_g25 = len([c for c in clusters if ... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
How to pick a reasonable cutoff?Since the clustering result depends on the threshold chosen by the user, we will have a closer look on the choice of a cutoff. | for i in numpy.arange(0., 1.0, 0.1):
clusters = ClusterFps(fingerprints,cutoff=i)
fig = plt.figure(1, figsize=(10, 4))
plt1 = plt.subplot(111)
plt.axis([0, len(clusters), 0, len(clusters[0])+1])
plt.xlabel('Cluster index', fontsize=20)
plt.ylabel('Number of molecules', fontsize=20)
plt.tick_... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
As you can see, the higher the threshold (distance cutoff), the more molecules are considered as similar and, therefore, clustered into less clusters.The lower the threshold, the more small clusters and "singletons" appear.* The smaller the distance value cut-off, the more similar the compounds are required to be to be... | dist_co = 0.2
clusters = ClusterFps(fingerprints,cutoff=dist_co)
# Plot the size of the clusters - save plot
fig = plt.figure(1, figsize=(8, 2.5))
plt1 = plt.subplot(111)
plt.axis([0, len(clusters), 0, len(clusters[0])+1])
plt.xlabel('Cluster index', fontsize=20)
plt.ylabel('# molecules', fontsize=20)
plt.tick_params(... | Number of clusters 1225 from 4925 molecules at distance cut-off 0.20
Number of molecules in largest cluster: 146
Similarity between two random points in same cluster 0.82
Similarity between two random points in different cluster 0.22
| CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Cluster visualization 10 examples from largest clusterNow, let's have a closer look at the first 10 molecular structures of the first/largest clusters. | print ('Ten molecules from largest cluster:')
# Draw molecules
Draw.MolsToGridImage([mols[i][0] for i in clusters[0][:10]],
legends=[mols[i][1] for i in clusters[0][:10]],
molsPerRow=5)
# Save molecules from largest cluster for MCS analysis in Talktorial 9
w = Chem.SDWriter('... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
10 examples from second largest cluster | print ('Ten molecules from second largest cluster:')
# Draw molecules
Draw.MolsToGridImage([mols[i][0] for i in clusters[1][:10]],
legends=[mols[i][1] for i in clusters[1][:10]],
molsPerRow=5) | Ten molecules from second largest cluster:
| CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
The first ten molecules in the respective clusters look indeed similar to each other and many share a common scaffold (visually detected). See **talktorial 6** for more information on how to calculate the maximum common substructure (MCS) of a set of molecules. Examples from first 10 clustersFor comparison, we have a ... | print ('Ten molecules from first 10 clusters:')
# Draw molecules
Draw.MolsToGridImage([mols[clusters[i][0]][0] for i in range(10)],
legends=[mols[clusters[i][0]][1] for i in range(10)],
molsPerRow=5) | Ten molecules from first 10 clusters:
| CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Save cluster centers from first 3 clusters as SVG file. | # Generate image
img = Draw.MolsToGridImage([mols[clusters[i][0]][0] for i in range(0,3)],
legends=["Cluster "+str(i) for i in range(1,4)],
subImgSize=(200,200), useSVG=True)
# Get SVG data
molsvg = img.data
# Replace non-transparent to transparent background and set font siz... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
While still some similarity is visible, clearly, the centroids from the different clusters look more dissimilar then the compounds within one cluster. Intra-cluster Tanimoto similaritiesWe can also have a look at the intra-cluster Tanimoto similarities. | # Function to compute Tanimoto similarity for all pairs of fingerprints in each cluster
def IntraTanimoto(fps_clusters):
intra_similarity =[]
# Calculate intra similarity per cluster
for k in range(0,len(fps_clusters)):
# Tanimoto distance matrix function converted to similarity matrix (1-distance)
... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Compound pickingIn the following, we are going to pick a final list of **max. 1000 compounds** as a **diverse** subset. For this, we take the cluster centroid from each cluster (i.e. the first molecule of each cluster) and then we take - starting with the largest cluster - for each cluster the 10 molecules (or 50% if ... | # Get the cluster center of each cluster (first molecule in each cluster)
clus_center = [mols[c[0]] for c in clusters]
# How many cluster centers/clusters do we have?
print('Number of cluster centers: ', len(clus_center)) | Number of cluster centers: 1225
| CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Sort clusters by size and molecules in each cluster by similarity. | # Sort the molecules within a cluster based on their similarity
# to the cluster center and sort the clusters based on their size
clusters_sort = []
for c in clusters:
if len(c) < 2: continue # Singletons
else:
# Compute fingerprints for each cluster element
fps_clust = [rdkit_gen.GetFingerprin... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Pick a maximum of 1000 compounds. | # Count selected molecules, pick cluster centers first
sel_molecules = clus_center.copy()
# Take 10 molecules (or a maximum of 50%) of each cluster starting with the largest one
index = 0
diff = 1000 - len(sel_molecules)
while diff > 0 and index < len(clusters_sort):
# Take indices of sorted clusters
tmp_clust... | # Selected molecules: 1225
| CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
This set of diverse molecules could now be used for experimental testing. (Additional information: run times)At the end of the talktorial, we can play with the size of the dataset and see how the Butina clustering run time changes. | # Reuse old dataset
sampled_mols = mols.copy() | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Note that you can try out larger datasets, but data sizes larger than 10000 data points already start to consume quite some memory and time (that's why we stopped there). | # Helper function for time computation
def MeasureRuntime(sampled_mols):
start_time = time.time()
sampled_fingerprints = [rdkit_gen.GetFingerprint(m) for m,idx in sampled_mols]
# Run the clustering with the dataset
sampled_clusters = ClusterFps(sampled_fingerprints,cutoff=0.3)
return(time.time() - ... | _____no_output_____ | CC-BY-4.0 | talktorials/5_compound_clustering/T5_compound_clustering.ipynb | caramirezs/TeachOpenCADD |
Copyright 2018 The TensorFlow Authors. | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Load text with tf.data View on TensorFlow.org Run in Google Colab View source on GitHub Download notebook This tutorial provides an example of how to use `tf.data.TextLineDataset` to load examples from text files. `TextLineDataset` is designed to create a dataset from a text file, in which... | from __future__ import absolute_import, division, print_function, unicode_literals
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
import tensorflow_datasets as tfds
import os | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
The texts of the three translations are by: - [William Cowper](https://en.wikipedia.org/wiki/William_Cowper) — [text](https://storage.googleapis.com/download.tensorflow.org/data/illiad/cowper.txt) - [Edward, Earl of Derby](https://en.wikipedia.org/wiki/Edward_Smith-Stanley,_14th_Earl_of_Derby) — [text](https://storage.... | DIRECTORY_URL = 'https://storage.googleapis.com/download.tensorflow.org/data/illiad/'
FILE_NAMES = ['cowper.txt', 'derby.txt', 'butler.txt']
for name in FILE_NAMES:
text_dir = tf.keras.utils.get_file(name, origin=DIRECTORY_URL+name)
parent_dir = os.path.dirname(text_dir)
parent_dir | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Load text into datasetsIterate through the files, loading each one into its own dataset.Each example needs to be labeled individually labeled, so use `tf.data.Dataset.map` to apply a labeler function to each one. This will iterate over every example in the dataset, returning (`example, label`) pairs. | def labeler(example, index):
return example, tf.cast(index, tf.int64)
labeled_data_sets = []
for i, file_name in enumerate(FILE_NAMES):
lines_dataset = tf.data.TextLineDataset(os.path.join(parent_dir, file_name))
labeled_dataset = lines_dataset.map(lambda ex: labeler(ex, i))
labeled_data_sets.append(labeled... | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Combine these labeled datasets into a single dataset, and shuffle it. | BUFFER_SIZE = 50000
BATCH_SIZE = 64
TAKE_SIZE = 5000
all_labeled_data = labeled_data_sets[0]
for labeled_dataset in labeled_data_sets[1:]:
all_labeled_data = all_labeled_data.concatenate(labeled_dataset)
all_labeled_data = all_labeled_data.shuffle(
BUFFER_SIZE, reshuffle_each_iteration=False) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
You can use `tf.data.Dataset.take` and `print` to see what the `(example, label)` pairs look like. The `numpy` property shows each Tensor's value. | for ex in all_labeled_data.take(5):
print(ex) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Encode text lines as numbersMachine learning models work on numbers, not words, so the string values need to be converted into lists of numbers. To do that, map each unique word to a unique integer. Build vocabularyFirst, build a vocabulary by tokenizing the text into a collection of individual unique words. There are... | tokenizer = tfds.features.text.Tokenizer()
vocabulary_set = set()
for text_tensor, _ in all_labeled_data:
some_tokens = tokenizer.tokenize(text_tensor.numpy())
vocabulary_set.update(some_tokens)
vocab_size = len(vocabulary_set)
vocab_size | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Encode examplesCreate an encoder by passing the `vocabulary_set` to `tfds.features.text.TokenTextEncoder`. The encoder's `encode` method takes in a string of text and returns a list of integers. | encoder = tfds.features.text.TokenTextEncoder(vocabulary_set) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
You can try this on a single line to see what the output looks like. | example_text = next(iter(all_labeled_data))[0].numpy()
print(example_text)
encoded_example = encoder.encode(example_text)
print(encoded_example) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Now run the encoder on the dataset by wrapping it in `tf.py_function` and passing that to the dataset's `map` method. | def encode(text_tensor, label):
encoded_text = encoder.encode(text_tensor.numpy())
return encoded_text, label
def encode_map_fn(text, label):
return tf.py_function(encode, inp=[text, label], Tout=(tf.int64, tf.int64))
all_encoded_data = all_labeled_data.map(encode_map_fn) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Split the dataset into text and train batchesUse `tf.data.Dataset.take` and `tf.data.Dataset.skip` to create a small test dataset and a larger training set.Before being passed into the model, the datasets need to be batched. Typically, the examples inside of a batch need to be the same size and shape. But, the example... | train_data = all_encoded_data.skip(TAKE_SIZE).shuffle(BUFFER_SIZE)
train_data = train_data.padded_batch(BATCH_SIZE, padded_shapes=([-1],[]))
test_data = all_encoded_data.take(TAKE_SIZE)
test_data = test_data.padded_batch(BATCH_SIZE, padded_shapes=([-1],[])) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Now, `test_data` and `train_data` are not collections of (`example, label`) pairs, but collections of batches. Each batch is a pair of (*many examples*, *many labels*) represented as arrays.To illustrate: | sample_text, sample_labels = next(iter(test_data))
sample_text[0], sample_labels[0] | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Since we have introduced a new token encoding (the zero used for padding), the vocabulary size has increased by one. | vocab_size += 1 | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Build the model | model = tf.keras.Sequential() | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
The first layer converts integer representations to dense vector embeddings. See the [Word Embeddings](../../tutorials/sequences/word_embeddings) tutorial for more details. | model.add(tf.keras.layers.Embedding(vocab_size, 64)) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
The next layer is a [Long Short-Term Memory](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) layer, which lets the model understand words in their context with other words. A bidirectional wrapper on the LSTM helps it to learn about the datapoints in relationship to the datapoints that came before it and aft... | model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64))) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Finally we'll have a series of one or more densely connected layers, with the last one being the output layer. The output layer produces a probability for all the labels. The one with the highest probability is the models prediction of an example's label. | # One or more dense layers.
# Edit the list in the `for` line to experiment with layer sizes.
for units in [64, 64]:
model.add(tf.keras.layers.Dense(units, activation='relu'))
# Output layer. The first argument is the number of labels.
model.add(tf.keras.layers.Dense(3, activation='softmax')) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Finally, compile the model. For a softmax categorization model, use `sparse_categorical_crossentropy` as the loss function. You can try other optimizers, but `adam` is very common. | model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy']) | _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Train the modelThis model running on this data produces decent results (about 83%). | model.fit(train_data, epochs=3, validation_data=test_data)
eval_loss, eval_acc = model.evaluate(test_data)
print('\nEval loss: {:.3f}, Eval accuracy: {:.3f}'.format(eval_loss, eval_acc))
| _____no_output_____ | Apache-2.0 | site/en/r2/tutorials/load_data/text.ipynb | crypdra/docs |
Introduction. Project is the continuation of web crawling of website fmovies's [most-watched](https://fmovies.to/most-watched) section analysis for the website. This is the second part. In part one we crawled websites and extracted informations. In part two we will tidy and clean the data for analysis in third part. | import pandas as pd
import numpy as np
movie_df = pd.read_csv('../Data/final_movies_df.csv')
tv_df = pd.read_csv('../Data/final_tvs_df.csv')
print(movie_df.columns)
print(tv_df.columns)
movie_df.head() | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Columns- 'movie_name/ tv_name' : Name of movie / tv - 'watch_link': Url link for page to watch movie/tv, - 'date_added': Date added to df not in fmovies- 'site_rank': Ranking in the fmovies by order of most watched starting from 1.- 'Genre': Genres- 'Stars': Cast,- 'IMDb': IMDb ratings,- 'Director': Director, - 'Relea... | movie_df.columns = movie_df.columns.str.upper().tolist()
tv_df.columns = tv_df.columns.str.upper().tolist()
tv_df.head(2)
movie_df.head(2) | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Tidying 1. Genre section has list of values in one row, lets make one value per row.2. Released Data can be converted to date time and then to index of df3. Ratings have to values, 1st is the site ratings and second is number of reviews by viewers. Lets separate them different columns. Genre Split and Date Column Let... | def split_genre(df):
cp= df.copy()
# Spilt the genre by "," and stack to make muliple rows each with own unique genre
# this will return a new df with genres only
genre= cp.GENRE.str.split(',').apply(pd.Series, 1).stack()
# Pop one of index
genre.index = genre.index.droplevel(-1)
... | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Ratings Columns Split | site_user_rating_4movie = movie_df_tidy_1.RATING.str.split("/").str[0]
site_number_user_rated_4movie = movie_df_tidy_1.RATING.str.split("/").str[1].str.split(" ").str[0]
site_user_rating_4tv = tv_df_tidy_1.RATING.str.split("/").str[0]
site_number_user_rated_4tv = tv_df_tidy_1.RATING.str.split("/").str[1].str.split(" "... | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Assign New cols and Drop the olds | tv_df_tidy_2 = tv_df_tidy_1.copy()
movie_df_tidy_2= movie_df_tidy_1.copy()
movie_df_tidy_2['User_Reviews_local'] = site_user_rating_4movie
movie_df_tidy_2['Number_Reviews_local'] = site_number_user_rated_4movie
tv_df_tidy_2['User_Reviews_local'] = site_user_rating_4tv
tv_df_tidy_2['Number_Reviews_local'] = site_number_... | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Missing Vlaues | print(movie_df_tidy_2.info())
print("**"*20)
print(tv_df_tidy_2.info()) | <class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 3790 entries, 2019-04-22 to 2007-02-09
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 MOVIE_NAME 3790 non-null object
1 WATCH_LINK 3790 non-null ... | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
It seems only movies has null vaules, lets dive deeper. | movie_df_tidy_2[movie_df_tidy_2.GENRE.isnull()] | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Earlier to prevent prolongation of crawling, we returned nan for bad requests. We can individually go throguh each link to values but lets drop them for now. | movie_df_tidy_2.dropna(inplace=True,axis=0) | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Write file for analysis part Index false argument on write will remove date index so lets not do that. | movie_df_tidy_2.to_csv('../Data/Movie.csv')
tv_df_tidy_2.to_csv('../Data/TV.csv') | _____no_output_____ | MIT | Files/.ipynb_checkpoints/fmovies_tidy-checkpoint.ipynb | nibukdk/web-scrapping-fmovie.to |
Copyright (c) Microsoft Corporation. All rights reserved.Licensed under the MIT License.  Configuration_**Setting up your Azure Machine Learning services workspace and configuring your notebook... | import azureml.core
print("This notebook was created using version 1.0.48
of the Azure ML SDK")
print("You are currently using version", azureml.core.VERSION, "of the Azure ML SDK") | _____no_output_____ | MIT | configuration.ipynb | mesameki/MachineLearningNotebooks |
If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK. 3. Azure Container Instance registrationAzure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subs... | import os
subscription_id = os.getenv("SUBSCRIPTION_ID", default="<my-subscription-id>")
resource_group = os.getenv("RESOURCE_GROUP", default="<my-resource-group>")
workspace_name = os.getenv("WORKSPACE_NAME", default="<my-workspace-name>")
workspace_region = os.getenv("WORKSPACE_REGION", default="eastus2") | _____no_output_____ | MIT | configuration.ipynb | mesameki/MachineLearningNotebooks |
Access your workspaceThe following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters. If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method. The cell can fail if the specified worksp... | from azureml.core import Workspace
try:
ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)
# write the details of the workspace to a configuration file to the notebook library
ws.write_config()
print("Workspace configuration succeeded. Sk... | _____no_output_____ | MIT | configuration.ipynb | mesameki/MachineLearningNotebooks |
Create a new workspaceIf you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace. If you don't have a resource group, the create workspace command will create one for you using the name you provide.**Note**: As with other Azure services, there are l... | from azureml.core import Workspace
# Create the workspace using the specified parameters
ws = Workspace.create(name = workspace_name,
subscription_id = subscription_id,
resource_group = resource_group,
location = workspace_region,
... | _____no_output_____ | MIT | configuration.ipynb | mesameki/MachineLearningNotebooks |
Create compute resources for your training experimentsMany of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.To creat... | from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
# Choose a name for your CPU cluster
cpu_cluster_name = "cpu-cluster"
# Verify that cluster does not exist already
try:
cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)
pri... | _____no_output_____ | MIT | configuration.ipynb | mesameki/MachineLearningNotebooks |
To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). | from azureml.core.compute import ComputeTarget, AmlCompute
from azureml.core.compute_target import ComputeTargetException
# Choose a name for your GPU cluster
gpu_cluster_name = "gpu-cluster"
# Verify that cluster does not exist already
try:
gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)
pri... | _____no_output_____ | MIT | configuration.ipynb | mesameki/MachineLearningNotebooks |
Importing the images into this script | import os
import numpy as np
directory = 'C:/Users/joaovitor/Desktop/Meu_Canal/DINO/'
jump_img = os.listdir(os.path.join(directory, 'jump'))
nojump_img = os.listdir(os.path.join(directory, 'no_jump'))
#checking if the number of images in both directories are equals
print(len(jump_img) == len(nojump_img))
print(len(ju... | False
81
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Storing the images array into lists | import cv2
imgs_list_jump = []
imgs_list_nojump = []
for img in jump_img:
images = cv2.imread(os.path.join(directory, 'jump', img), 0) #0 to convert the image to grayscale
imgs_list_jump.append(images)
for img in nojump_img:
images = cv2.imread(os.path.join(directory, 'no_jump', img), 0) #0 to conver... | [[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
...
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]
[0 0 0 ... 0 0 0]]
==================================================
Images Dimensions: (480, 640)
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Let's display the first image | import matplotlib.pyplot as plt
img = cv2.cvtColor(imgs_list_jump[0], cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.show() | _____no_output_____ | MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
The images have 480 pixels height and 640 pixels width | print(imgs_list_jump[0].shape) | (480, 640)
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
The images sizes still very big, so we are going to resize all images in order to make them smaller | print('Original size:', imgs_list_jump[0].size) #original size | Original size: 307200
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
We will apply the code bellow to all images | scale_percent = 20 #20 percent of original size
width = int(imgs_list_jump[0].shape[1] * scale_percent / 100)
height = int(imgs_list_jump[0].shape[0] * scale_percent / 100)
dim = (width, height)
#resize image
resized = cv2.resize(imgs_list_jump[0], dim, interpolation = cv2.INTER_AREA)
print('Original Dimensions:', ... | Original Dimensions: (480, 640)
Resized Dimensions: (96, 128)
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Applying to all images | scale_percent = 20 # 20 percent of original size
resized_jump_list = []
resized_nojump_list = []
for img in imgs_list_jump:
width = int(img.shape[1] * scale_percent / 100)
height = int(img.shape[0] * scale_percent / 100)
dim = (width, height)
#resize image
resized = cv2.resize(img, dim, interpola... | (96, 128)
(96, 128)
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Creating my X dataset | nojump_list_reshaped = []
jump_list_reshaped = []
for img in resized_nojump_list:
nojump_list_reshaped.append(img.reshape(-1, img.size))
for img in resized_jump_list:
jump_list_reshaped.append(img.reshape(-1, img.size))
X_nojump = np.array(nojump_list_reshaped).reshape(len(nojump_list_reshaped), nojump_list_... | (386, 12288)
(81, 12288)
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Joining both X's | X = np.vstack([X_nojump, X_jump])
print(X.shape) | (467, 12288)
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Creating my Y dataset | y_nojump = np.array([0 for i in range(len(nojump_list_reshaped))]).reshape(len(nojump_list_reshaped),-1)
y_jump = np.array([1 for i in range(len(jump_list_reshaped))]).reshape(len(jump_list_reshaped),-1) | _____no_output_____ | MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Joining both Y's | y = np.vstack([y_nojump, y_jump])
print(y.shape) | (467, 1)
| MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Shuffling both datasets | shuffle_index = np.random.permutation(y.shape[0])
#print(shuffle_index)
X, y = X[shuffle_index], y[shuffle_index] | _____no_output_____ | MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Creating a X_train and y_train dataset | X_train = X
y_train = y | _____no_output_____ | MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Choosing SVM (Support Vector Machine) as our Machine Learning model | from sklearn.svm import SVC
svm_clf = SVC(kernel='linear')
svm_clf.fit(X_train, y_train.ravel()) | _____no_output_____ | MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Creating a confusion matrix to evaluate the model performance | from sklearn.model_selection import cross_val_predict
from sklearn.metrics import confusion_matrix
y_train_pred = cross_val_predict(svm_clf, X_train, y_train.ravel(), cv=3) #sgd_clf no primeiro parametro
confusion_matrix(y_train.ravel(), y_train_pred) | _____no_output_____ | MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Saving the model | import joblib
joblib.dump(svm_clf, 'jump_model.pkl') #sgd_clf no primeiro parametro | _____no_output_____ | MIT | Pygame-master/Chrome_Dinosaur_Game/MACHINE_LEARNING.ipynb | professorjar/curso-de-jogos- |
Reflect Tables into SQLAlchemy ORM | # Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
# create engine to hawaii.sqlite
engine = create_engine("sqlite:///hawaii.sqlite")
# reflect an existing database into a new m... | _____no_output_____ | ADSL | climate_starter.ipynb | tanmayrp/sqlalchemy-challenge |
Exploratory Precipitation Analysis | # Find the most recent date in the data set.
most_recent_date_str = session.query(Measurement.date).order_by(Measurement.date.desc()).first()
print(f"The most recent date in the data set: {most_recent_date_str[0]}")
# Design a query to retrieve the last 12 months of precipitation data and plot the results.
# Starting ... | _____no_output_____ | ADSL | climate_starter.ipynb | tanmayrp/sqlalchemy-challenge |
Exploratory Station Analysis | # Design a query to calculate the total number stations in the dataset
print(f"The number of stations in the dataset: {session.query(Station.id).count()} ");
# Design a query to find the most active stations (i.e. what stations have the most rows?)
# List the stations and the counts in descending order.
most_active_sta... | _____no_output_____ | ADSL | climate_starter.ipynb | tanmayrp/sqlalchemy-challenge |
Close session | # Close Session
session.close() | _____no_output_____ | ADSL | climate_starter.ipynb | tanmayrp/sqlalchemy-challenge |
Binary Logistic RegressionLet $X$ training input of size $n * p$. It contains $n$ examples, each with $p$ features. Let $y$ training target of size $n$. Each input $X_i$, vector of size $p$, is associated with it's target, $y_i$, which is $0$ or $1$. Logistic regression tries to fit a linear model to predict the t... | def sigmoid(x):
return 1 / (1 + np.exp(-x))
y_out = np.random.randn(13).astype(np.float32)
y_true = np.random.randint(0, 2, (13)).astype(np.float32)
y_pred = sigmoid(y_out)
j = - np.sum(y_true * np.log(y_pred) + (1-y_true) * np.log(1-y_pred))
ty_true = torch.tensor(y_true, requires_grad=False)
ty_pred = torch.ten... | [-1.6231388 -2.9766939 2.274354 -6.4779763 -1.4708843 1.2155157
-1.9948862 1.8867183 1.4462028 18.669147 1.5500078 -1.6234685
-1.3342199]
[-1.6231389 -2.976694 2.274354 -6.477976 -1.4708843 1.2155157
-1.9948862 1.8867184 1.4462028 18.669147 1.5500077 -1.6234685
-1.3342199]
5.717077e-07
| MIT | courses/ml/logistic_regression.ipynb | obs145628/ml-notebooks |
$$\frac{\partial J(\beta)}{\partial o_i} = \hat{y_i} - y_i$$$$\frac{\partial J(\beta)}{\partial o} = \hat{y} - y$$ | y_out = np.random.randn(13).astype(np.float32)
y_true = np.random.randint(0, 2, (13)).astype(np.float32)
y_pred = sigmoid(y_out)
j = - np.sum(y_true * np.log(y_pred) + (1-y_true) * np.log(1-y_pred))
ty_true = torch.tensor(y_true, requires_grad=False)
ty_out = torch.tensor(y_out, requires_grad=True)
criterion = torch.n... | [-0.7712122 0.5310385 -0.7378207 -0.13447696 0.20648097 0.28622478
-0.7465389 0.5608791 0.53383535 -0.75912154 -0.4418677 0.6848638
0.35961235]
[-0.7712122 0.5310385 -0.7378207 -0.13447696 0.20648097 0.28622478
-0.7465389 0.5608791 0.53383535 -0.75912154 -0.4418677 0.6848638
0.35961235]
0.... | MIT | courses/ml/logistic_regression.ipynb | obs145628/ml-notebooks |
Can be trained with gradient descent | def log_reg_sk(X, y):
m = LogisticRegression(fit_intercept=False)
m.fit(X, y)
return m.coef_
def get_error(X, y, w):
y_pred = sigmoid(X @ w)
err = - np.sum(y * np.log(y_pred) + (1-y) * np.log(1-y_pred))
return err
def log_reg(X, y):
w = np.random.randn(X.shape[1])
for epoch ... | SGD Error = 71.14744133609668
SGD Error = 49.65028785288255
SGD Error = 48.91772028291884
SGD Error = 48.888462052036814
SGD Error = 48.88680421514018
SGD Error = 48.88669058552164
SGD Error = 48.88668168135676
SGD Error = 48.886680916022215
SGD Error = 48.88668084643879
SGD Error = 48.88668083991474
SGD Error = 48.886... | MIT | courses/ml/logistic_regression.ipynb | obs145628/ml-notebooks |
Multiclass Logistic Regression | def softmax(x):
x_e = np.exp(x)
return x_e / np.sum(x_e, axis=1, keepdims=True)
y_out = np.random.randn(93, 4).astype(np.float32)
y_true = np.zeros((93, 4)).astype(np.float32)
for i in range(y_true.shape[0]):
y_true[i][np.random.randint(0, y_true.shape[1])] = 1
y_pred = softmax(y_out)
j = - np.sum(y_true *... | [[ -0. -10.283339 -0. -0. ]
[-10.58094 -0. -0. -0. ]
[ -0. -0. -2.7528124 -0. ]
[-46.90987 -0. -0. -0. ]
[ -0. -0. -1.3170731 -0. ]
[ -7.9531765 -0. -0. -0. ]
[ -0. ... | MIT | courses/ml/logistic_regression.ipynb | obs145628/ml-notebooks |
$$\frac{\partial J(\beta)}{\partial o_{ij}} = \hat{y_{ij}} - y_{ij}$$$$\frac{\partial J(\beta)}{\partial o} = \hat{y} - y$$ | y_out = np.random.randn(7, 4).astype(np.float32)
y_true = np.zeros((7, 4)).astype(np.float32)
for i in range(y_true.shape[0]):
y_true[i][np.random.randint(0, y_true.shape[1])] = 1
y_pred = softmax(y_out)
j = - np.sum(y_true * np.log(y_pred))
ty_true = torch.tensor(y_true, requires_grad=False)
ty_true = torch.argm... | [[-0.71088123 0.25399554 0.31700996 0.13987577]
[ 0.02140404 0.3097546 0.29681578 -0.6279745 ]
[ 0.60384715 0.03253903 0.0066169 -0.6430031 ]
[ 0.22169167 -0.88766754 0.03120301 0.63477284]
[ 0.05100057 -0.38170385 0.10363309 0.22707026]
[ 0.02778155 0.6928965 -0.8194856 0.09880757]
[ 0.03780703 ... | MIT | courses/ml/logistic_regression.ipynb | obs145628/ml-notebooks |
Can be trained with gradient descent | def get_error_multi(X, y, w):
y_pred = softmax(X @ w)
err = - np.sum(y * np.log(y_pred))
return err
def multilog_reg(X, y):
w = np.random.randn(X.shape[1], y.shape[1])
for epoch in range(10000):
y_pred = softmax(X @ w)
dy_out = y_pred - y
dw = X.T @ dy_out
... | SGD Error = 264.5967568728954
SGD Error = 124.52928999771657
SGD Error = 120.69338069535253
SGD Error = 120.60511291188504
SGD Error = 120.60208822782775
SGD Error = 120.60195961583351
SGD Error = 120.60195360857097
SGD Error = 120.60195331813674
SGD Error = 120.60195330392729
SGD Error = 120.60195330322918
SGD Error =... | MIT | courses/ml/logistic_regression.ipynb | obs145628/ml-notebooks |
Scaffolds of Keck_Pria_FP_data | Target_name = 'Keck_Pria_FP_data'
smiles_list = []
for i in range(k):
smiles_list.extend(data_pd_list[i][data_pd_list[i][Target_name]==1]['SMILES'].tolist())
scaffold_set = set()
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
core = MurckoScaffold.GetScaffoldForMol(mol)
scaffold = Chem.Mol... | Original SMILES is c1cc(cc2c1CCCN2CCOC)NS(=O)(=O)c3c(c(c(c(c3C)C)C)C)C
The Scaffold is O=S(=O)(Nc1ccc2c(c1)NCCC2)c1ccccc1
Original SMILES is c1cc(ccc1CC)NC(=O)CSc2ncc(c(=O)[nH]2)S(=O)(=O)c3ccc(cc3C)C
The Scaffold is O=C(CSc1ncc(S(=O)(=O)c2ccccc2)c(=O)[nH]1)Nc1ccccc1
Original SMILES is c1ccc2c(c1)c(c[nH]2)CCNC(=O... | MIT | pria_lifechem/analysis/scaffold/scaffold_Keck_Pria_FP_data.ipynb | chao1224/pria_lifechem |
Below is scaffold for each fold Scaffold for fold 0 | i = 0
smiles_list = data_pd_list[i][data_pd_list[i][Target_name]==1]['SMILES'].tolist()
scaffold_set = set()
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
core = MurckoScaffold.GetScaffoldForMol(mol)
scaffold = Chem.MolToSmiles(core)
scaffold_set.add(scaffold)
print 'Original SMILES i... | Original SMILES is c1cc(cc2c1CCCN2CCOC)NS(=O)(=O)c3c(c(c(c(c3C)C)C)C)C
The Scaffold is O=S(=O)(Nc1ccc2c(c1)NCCC2)c1ccccc1
Original SMILES is c1cc(ccc1CC)NC(=O)CSc2ncc(c(=O)[nH]2)S(=O)(=O)c3ccc(cc3C)C
The Scaffold is O=C(CSc1ncc(S(=O)(=O)c2ccccc2)c(=O)[nH]1)Nc1ccccc1
Original SMILES is c1ccc2c(c1)c(c[nH]2)CCNC(=O... | MIT | pria_lifechem/analysis/scaffold/scaffold_Keck_Pria_FP_data.ipynb | chao1224/pria_lifechem |
Scaffold for fold 1 | i = 1
smiles_list = data_pd_list[i][data_pd_list[i][Target_name]==1]['SMILES'].tolist()
scaffold_set = set()
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
core = MurckoScaffold.GetScaffoldForMol(mol)
scaffold = Chem.MolToSmiles(core)
scaffold_set.add(scaffold)
print 'Original SMILES i... | Original SMILES is c1cc(cc(c1)Cl)Nc2nc(cs2)CC(=O)Nc3ccc4c(c3)OCCO4
The Scaffold is O=C(Cc1csc(Nc2ccccc2)n1)Nc1ccc2c(c1)OCCO2
Original SMILES is c1cc(cc(c1NC(=O)c2c(nns2)C)[N+](=O)[O-])OCC
The Scaffold is O=C(Nc1ccccc1)c1cnns1
Original SMILES is c1ccc2c(c1)ccn2CCNC(=S)NCCc3cc4ccc(cc4[nH]c3=O)C
The Scaffold is O=... | MIT | pria_lifechem/analysis/scaffold/scaffold_Keck_Pria_FP_data.ipynb | chao1224/pria_lifechem |
Scaffold for fold 2 | i = 2
smiles_list = data_pd_list[i][data_pd_list[i][Target_name]==1]['SMILES'].tolist()
scaffold_set = set()
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
core = MurckoScaffold.GetScaffoldForMol(mol)
scaffold = Chem.MolToSmiles(core)
scaffold_set.add(scaffold)
print 'Original SMILES i... | Original SMILES is c1ccc(c(c1)C(=O)Nc2nnc(o2)Cc3cccs3)SCC
The Scaffold is O=C(Nc1nnc(Cc2cccs2)o1)c1ccccc1
Original SMILES is c1cc(ccc1n2ccnc2SCC(=O)Nc3ccc(cc3)Br)F
The Scaffold is O=C(CSc1nccn1-c1ccccc1)Nc1ccccc1
Original SMILES is c1cc2c(cc1C(=O)NCc3ccc4c(c3)cc(n4C)C)OCO2
The Scaffold is O=C(NCc1ccc2[nH]ccc2c1... | MIT | pria_lifechem/analysis/scaffold/scaffold_Keck_Pria_FP_data.ipynb | chao1224/pria_lifechem |
Scaffold for fold 3 | i = 3
smiles_list = data_pd_list[i][data_pd_list[i][Target_name]==1]['SMILES'].tolist()
scaffold_set = set()
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
core = MurckoScaffold.GetScaffoldForMol(mol)
scaffold = Chem.MolToSmiles(core)
scaffold_set.add(scaffold)
print 'Original SMILES i... | Original SMILES is c1cc(oc1)C(=O)Nc2ccc(cc2)Nc3ccc(nn3)n4cccn4
The Scaffold is O=C(Nc1ccc(Nc2ccc(-n3cccn3)nn2)cc1)c1ccco1
Original SMILES is c1ccc(c(c1)C(=O)Nc2nc(cs2)c3ccccn3)Br
The Scaffold is O=C(Nc1nc(-c2ccccn2)cs1)c1ccccc1
Original SMILES is c1ccc(cc1)C2=NN(C(C2)c3ccc4c(c3)nccn4)C(=O)c5cccs5
The Scaffold is... | MIT | pria_lifechem/analysis/scaffold/scaffold_Keck_Pria_FP_data.ipynb | chao1224/pria_lifechem |
Scaffold for fold 4 | i = 4
smiles_list = data_pd_list[i][data_pd_list[i][Target_name]==1]['SMILES'].tolist()
scaffold_set = set()
for smiles in smiles_list:
mol = Chem.MolFromSmiles(smiles)
core = MurckoScaffold.GetScaffoldForMol(mol)
scaffold = Chem.MolToSmiles(core)
scaffold_set.add(scaffold)
print 'Original SMILES i... | Original SMILES is c1cc(sc1)Cc2nnc(o2)NC(=O)c3ccc(cc3)S(=O)(=O)N(C)CCCC
The Scaffold is O=C(Nc1nnc(Cc2cccs2)o1)c1ccccc1
Original SMILES is c1cc2cccnc2c(c1)SCC(=O)NCCc3ccc(cc3)Cl
The Scaffold is O=C(CSc1cccc2cccnc12)NCCc1ccccc1
Original SMILES is c1cc(cc(c1)F)NC(=O)Nc2ccc(cc2)Nc3ccc(nn3)n4cccn4
The Scaffold is O... | MIT | pria_lifechem/analysis/scaffold/scaffold_Keck_Pria_FP_data.ipynb | chao1224/pria_lifechem |
Задание 3.2 - сверточные нейронные сети (CNNs) в PyTorchЭто упражнение мы буде выполнять в Google Colab - https://colab.research.google.com/ Google Colab позволяет запускать код в notebook в облаке Google, где можно воспользоваться бесплатным GPU! Авторы курса благодарят компанию Google и надеятся, что праздник не з... | # Intstall PyTorch and download data
!pip3 install torch torchvision
!wget -c http://ufldl.stanford.edu/housenumbers/train_32x32.mat http://ufldl.stanford.edu/housenumbers/test_32x32.mat
from collections import namedtuple
import matplotlib.pyplot as plt
import numpy as np
import PIL
import torch
import torch.nn as nn... | _____no_output_____ | MIT | assignments/assignment3/PyTorch_CNN.ipynb | pavel2805/my_dlcoarse_ai |
Загружаем данные | # First, lets load the dataset
data_train = dset.SVHN('./',
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.43,0.44,0.47],
std=[0.20,0.20,0.20]) ... | _____no_output_____ | MIT | assignments/assignment3/PyTorch_CNN.ipynb | pavel2805/my_dlcoarse_ai |
Разделяем данные на training и validation.На всякий случай для подробностей - https://pytorch.org/tutorials/beginner/data_loading_tutorial.html | batch_size = 64
data_size = data_train.data.shape[0]
validation_split = .2
split = int(np.floor(validation_split * data_size))
indices = list(range(data_size))
np.random.shuffle(indices)
train_indices, val_indices = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_indices)
val_sampler = Sub... | _____no_output_____ | MIT | assignments/assignment3/PyTorch_CNN.ipynb | pavel2805/my_dlcoarse_ai |
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