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 |
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**Expected Output**: **gradients["dxt"][1][2]** = 3.23055911511 **gradients["dxt"].shape** = (3, 10) **gradients["da_prev"][2][3]** = -0.0639... | def lstm_backward(da, caches):
"""
Implement the backward pass for the RNN with LSTM-cell (over a whole sequence).
Arguments:
da -- Gradients w.r.t the hidden states, numpy-array of shape (n_a, m, T_x)
dc -- Gradients w.r.t the memory states, numpy-array of shape (n_a, m, T_x)
caches -- ca... | gradients["dx"][1][2] = [-0.00173313 0.08287442 -0.30545663 -0.43281115]
gradients["dx"].shape = (3, 10, 4)
gradients["da0"][2][3] = -0.095911501954
gradients["da0"].shape = (5, 10)
gradients["dWf"][3][1] = -0.0698198561274
gradients["dWf"].shape = (5, 8)
gradients["dWi"][1][2] = 0.102371820249
gradients["dWi"].shape ... | MIT | Course-5-Sequence-Models/week1/Building+a+Recurrent+Neural+Network+-+Step+by+Step+-+v3.ipynb | xnone/coursera-deep-learning |
Azure ML & Azure Databricks notebooks by Parashar Shah.Copyright (c) Microsoft Corporation. All rights reserved.Licensed under the MIT License.  Model Building | import os
import pprint
import numpy as np
from pyspark.ml import Pipeline, PipelineModel
from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.ml.tuning import C... | _____no_output_____ | MIT | how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb | keerthiadu/MachineLearningNotebooks |
Define Model | label = "income"
dtypes = dict(train.dtypes)
dtypes.pop(label)
si_xvars = []
ohe_xvars = []
featureCols = []
for idx,key in enumerate(dtypes):
if dtypes[key] == "string":
featureCol = "-".join([key, "encoded"])
featureCols.append(featureCol)
tmpCol = "-".join([key, "tmp"])
... | _____no_output_____ | MIT | how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb | keerthiadu/MachineLearningNotebooks |
Model Evaluation | ##unzip the model to dbfs (as load() seems to require that) and load it.
if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):
shutil.rmtree(model_dbfs)
shutil.unpack_archive(best_model_file_name, model_dbfs)
model_p_best = PipelineModel.load(model_name)
# make prediction
pred = model_p_best.transform(test)
... | _____no_output_____ | MIT | how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb | keerthiadu/MachineLearningNotebooks |
Model Persistence | ##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!
model_p_best.write().overwrite().save(model_name)
print("saved model to {}".format(model_dbfs))
%sh
ls -la /dbfs/AdultCensus_runHistory.mml/*
dbutils.notebook.exit("success") | _____no_output_____ | MIT | how-to-use-azureml/azure-databricks/03.Build_model_runHistory.ipynb | keerthiadu/MachineLearningNotebooks |
**Installing the transformers library** | !cat /proc/meminfo
!df -h
!pip install transformers | Collecting transformers
[?25l Downloading https://files.pythonhosted.org/packages/d8/f4/9f93f06dd2c57c7cd7aa515ffbf9fcfd8a084b92285732289f4a5696dd91/transformers-3.2.0-py3-none-any.whl (1.0MB)
[K |████████████████████████████████| 1.0MB 9.0MB/s
[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/loca... | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Importing the tools** | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import torch
import transformers as ppb
from sklearn.preprocessing import L... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Importing the dataset from Drive** | from google.colab import drive
drive.mount('/content/gdrive')
df=pd.read_csv('gdrive/My Drive/Total_cleaned.csv',delimiter=';')
df1=pd.read_csv('gdrive/My Drive/Final_DUP.csv',delimiter=';')
df2=pd.read_csv('gdrive/My Drive/Final_NDUP.csv',delimiter=';')
df[3] | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Loading the Pre-trained BERT model** | model_class, tokenizer_class, pretrained_weights = (ppb.BertModel, ppb.BertTokenizer, 'bert-base-uncased')
tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
model = model_class.from_pretrained(pretrained_weights)
model_class, tokenizer_class, pretrained_weights = (ppb.DistilBertModel, ppb.DistilBertTokeni... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Lower case** | df[0]= df[0].str.lower()
df[1]= df[1].str.lower()
df[2]= df[2].str.lower()
df[3]= df[3].str.lower()
df[4]= df[4].str.lower()
df[5]= df[5].str.lower() | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Remove Digits** | df[3] = df[3].str.replace(r'0', '')
df[3] = df[3].str.replace(r'1', '')
df[3] = df[3].str.replace(r'2', '')
df[3] = df[3].str.replace(r'3', '')
df[3] = df[3].str.replace(r'4', '')
df[3] = df[3].str.replace(r'5', '')
df[3] = df[3].str.replace(r'6', '')
df[3] = df[3].str.replace(r'7', '')
df[3] = df[3].str.replace(r'8', ... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Remove special characters** | df[3] = df[3].str.replace(r'/', '')
df[3] = df[3].str.replace(r'@ ?', '')
df[3] = df[3].str.replace(r'!', '')
df[3] = df[3].str.replace(r'+', '')
df[3] = df[3].str.replace(r'-', '')
df[3] = df[3].str.replace(r'/', '')
df[3] = df[3].str.replace(r':', '')
df[3] = df[3].str.replace(r';', '')
df[3] = df[3].str.replace(r'>'... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Convert to String type** | df[3] = pd.Series(df[3], dtype="string") # Pblm tokenize : " Input is not valid ,Should be a string, a list/tuple of strings or a list/tuple of integers"
df[2] = pd.Series(df[2], dtype="string")
df[2] = df[2].astype("|S")
df[2].str.decode("utf-8")
df[3] = df[3].astype("|S")
df[3].str.decode("utf-8")
df[3].str.len()
| _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Tokenization** | df.shape
batch_31=df1[:3000]
batch_32=df2[:3000]
df3 = pd.concat([batch_31,batch_32], ignore_index=True)
batch_41=df1[3000:6000]
batch_42=df2[3000:6000]
df4 = pd.concat([batch_41,batch_42], ignore_index=True)
batch_51=df1[6000:9000]
batch_52=df2[6000:9000]
df5 = pd.concat([batch_51,batch_52], ignore_index=True)
batch_6... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df3** | pair3= df3['Title1'] + [" [SEP] "] + df3['Title2']
tokenized3 = pair3.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
max_len3 = 0 # padding all lists to the same size
for i in tokenized3.values:
if len(i) > max_len3:
max_len3 = len(i)
max_len... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df4** | pair4= df4['Title1'] + [" [SEP] "] + df4['Title2']
tokenized4 = pair4.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
max_len4 = 0 # padding all lists to the same size
for i in tokenized4.values:
if len(i) > max_len4:
max_len4 = len(i)
max_len... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df5** | pair5= df5['Title1'] + [" [SEP] "] + df5['Title2']
tokenized5 = pair5.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
pair5.shape
tokenized5.shape | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Padding** | max_len5 = 0 # padding all lists to the same size
for i in tokenized5.values:
if len(i) > max_len5:
max_len5 = len(i)
max_len5 =120
padded5 = np.array([i + [0]*(max_len5-len(i)) for i in tokenized5.values])
np.array(padded5).shape # Dimensions of the padded variable | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Masking** | attention_mask5 = np.where(padded5 != 0, 1, 0)
attention_mask5.shape
input_ids5 = torch.tensor(padded5)
attention_mask5 = torch.tensor(attention_mask5)
input_ids[0] ######## TITLE 2 | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Running the `model()` function through BERT** | def _get_segments3(tokens, max_seq_length):
"""Segments: 0 for the first sequence, 1 for the second"""
if len(tokens)>max_seq_length:
raise IndexError("Token length more than max seq length!")
segments = []
first_sep = False
current_segment_id = 0
for token in tokens:
segments.a... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Slicing the part of the output of BERT : [cls]** | features5 = last_hidden_states5[0][:,0,:].numpy()
features5 | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df6** | pair6= df6['Title1'] + [" [SEP] "] + df6['Title2']
tokenized6 = pair6.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
max_len6 = 0 # padding all lists to the same size
for i in tokenized6.values:
if len(i) > max_len6:
max_len6 = len(i)
max_len... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df7** | pair7= df7['Title1'] + [" [SEP] "] + df7['Title2']
tokenized7 = pair7.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
max_len7 = 0 # padding all lists to the same size
for i in tokenized7.values:
if len(i) > max_len7:
max_len7 = len(i)
max_len... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df8** | pair8= df8['Title1'] + [" [SEP] "] + df8['Title2']
tokenized8 = pair8.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
max_len8 = 0 # padding all lists to the same size
for i in tokenized8.values:
if len(i) > max_len8:
max_len8 = len(i)
max_len... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df9** | pair9= df9['Title1'] + [" [SEP] "] + df9['Title2']
tokenized9 = pair9.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
max_len9 = 0 # padding all lists to the same size
for i in tokenized9.values:
if len(i) > max_len9:
max_len9 = len(i)
max_len... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**df10** | pair10= df10['Title1'] + [" [SEP] "] + df10['Title2']
tokenized10 = pair10.apply((lambda x: tokenizer.encode(x, add_special_tokens=True,truncation=True, max_length=512)))
max_len10 = 0 # padding all lists to the same size
for i in tokenized10.values:
if len(i) > max_len10:
max_len10 = len(i... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Classification** | features=np.concatenate([features3,features4,features5,features6,features7])
features=features3
features.shape
Total = pd.concat([df3,df4,df5,df6,df7], ignore_index=True)
Total
labels =df3['Label']
labels =Total['Label']
labels
train_features, test_features, train_labels, test_labels = train_test_split(features, labels... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Decision tree** | from sklearn.tree import DecisionTreeClassifier
#n_splits=2
#cross_val_score=5
parameters = {'C': max_leaf_nodes=0)}
grid_search = GridSearchCV(DecisionTreeClassifier(), parameters, cv=5)
grid_search.fit(train_features, train_labels)
print('best parameters: ', grid_search.best_params_)
print('best scrores: ', grid_sear... | mean: 0.814 (std: 0.012)
| MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**SVM** | from sklearn.svm import SVC
#n_splits=2
#cross_val_score=5
parameters = {'C': np.linspace(0.0001, 100, 20)}
grid_search = GridSearchCV(SVC(), parameters, cv=5)
grid_search.fit(train_features, train_labels)
print('best parameters: ', grid_search.best_params_)
print('best scrores: ', grid_search.best_score_)
svclassifie... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
Cross_Val_SVC | from sklearn.model_selection import cross_val_score
from sklearn import svm
clf = svm.SVC(kernel='linear', C=36.84)
scores = cross_val_score(clf, test_labels, y_pred, cv=5)
scores = cross_val_score(clf, test_features, test_labels, cv=10)
print("mean: {:.3f} (std: {:.3f})".format(scores.mean(),
... | mean: 0.850 (std: 0.011)
| MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**MLP Best params** | from sklearn.neural_network import MLPClassifier
mlp = MLPClassifier(max_iter=100)
from sklearn.datasets import make_classification
parameter_space = {
'hidden_layer_sizes': [(50,50,50), (50,100,50), (100,)],
'activation': ['tanh', 'relu'],
'solver': ['sgd', 'adam'],
'alpha': [0.0001, 0.05],
'learni... | mean: 0.872 (std: 0.014)
| MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Random Forest** | from sklearn.ensemble import RandomForestRegressor
regressor = RandomForestRegressor(n_estimators=20, random_state=0)
regressor.fit(train_features, train_labels)
y_pred1 = regressor.predict(test_features)
y_pred
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
print(confusion_matrix... | [[616 73]
[ 86 625]]
| MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Naive Bayes** Gaussian | from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(train_features, train_labels)
y_pred = gnb.predict(test_features)
from sklearn import metrics
print("Accuracy:",metrics.accuracy_score(test_labels, y_pred)) | Accuracy: 0.8358333333333333
| MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
*Cross Validation* |
print("Cross Validation:",cross_val_score(gnb, digits.data, digits.target, scoring='accuracy', cv=10).mean()) | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
Bernoulli: | from sklearn.naive_bayes import BernoulliNB
bnb = BernoulliNB(binarize=0.0)
bnb.fit(train_features, train_labels)
print("Score: ",bnb.score(test_features, test_labels)) | Score: 0.829
| MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
*Cross Validation* | print("Cross Validation:",cross_val_score(bnb, digits.data, digits.target, scoring='accuracy', cv=10).mean()) | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
Multinomial | from sklearn.naive_bayes import MultinomialNB
mnb = MultinomialNB()
mnb.fit(train_features, train_labels)
mnb.score(test_features, test_labels)
mnb = MultinomialNB()
cross_val_score(mnb, digits.data, digits.target, scoring='accuracy', cv=10).mean()
| _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
Best params SVC | from sklearn.svm import SVC
model = SVC()
model.fit(train_features, train_labels)
prediction = model.predict(test_features)
from sklearn.metrics import classification_report, confusion_matrix
print(classification_report(test_labels,prediction))
print(confusion_matrix(test_labels, prediction))
param_grid = {'C':[1,10,10... | precision recall f1-score support
0 0.86 0.95 0.90 587
1 0.95 0.85 0.90 613
accuracy 0.90 1200
macro avg 0.91 0.90 0.90 1200
weighted avg 0.91 0.90 0.90 ... | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
**Random Forest Best params** | from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier(random_state=42)
param_grid = {
'n_estimators': [200, 500],
'max_features': ['auto', 'sqrt', 'log2'],
'max_depth' : [4,5,6,7,8],
'criterion' :['gini', 'entropy']
}
CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid,... | _____no_output_____ | MIT | Copie de test_tokens(1).ipynb | asma-miladi/DupBugRep-Scripts |
Lambda School Data Science*Unit 2, Sprint 1, Module 4*--- Logistic Regression Assignment 🌯You'll use a [**dataset of 400+ burrito reviews**](https://srcole.github.io/100burritos/). How accurately can you predict whether a burrito is rated 'Great'?> We have developed a 10-dimensional system for rating the burritos in... | %%capture
import sys
# If you're on Colab:
if 'google.colab' in sys.modules:
DATA_PATH = 'https://raw.githubusercontent.com/LambdaSchool/DS-Unit-2-Linear-Models/master/data/'
!pip install category_encoders==2.*
# If you're working locally:
else:
DATA_PATH = '../data/'
# Load data downloaded from https://s... | _____no_output_____ | MIT | module4-logistic-regression/Logistic_Regression_Assignment.ipynb | afroman32/DS-Unit-2-Linear-Models |
**Train, Val, Test split** | df.shape
# reset index because it skipped a couple of numbers
df.reset_index(inplace = True, drop = True)
df.head(174)
# convert Great column to 1 for True and 0 for False
key = {True: 1, False:0}
df['Great'].replace(key, inplace = True)
# convert date column to datetime
df['Date'] = pd.to_datetime(df['Date'])
df.head... | count 298
unique 110
top 2016-08-30 00:00:00
freq 29
first 2011-05-16 00:00:00
last 2016-12-15 00:00:00
Name: Date, dtype: object
count 85
unique 42
top 2017-04-07 00:00:00
freq ... | MIT | module4-logistic-regression/Logistic_Regression_Assignment.ipynb | afroman32/DS-Unit-2-Linear-Models |
**Baseline** | target = 'Great'
features = ['Yelp', 'Google', 'Cost', 'Hunger', 'Tortilla', 'Temp', 'Meat',
'Fillings', 'Meat:filling', 'Uniformity', 'Salsa', 'Synergy', 'Wrap']
X_train = train[features]
y_train = train[target].astype(int)
X_val = val[features]
y_val = val[target].astype(int)
y_train.value_counts(norma... | _____no_output_____ | MIT | module4-logistic-regression/Logistic_Regression_Assignment.ipynb | afroman32/DS-Unit-2-Linear-Models |
**Logistic Regression Model** | import category_encoders as ce
from sklearn.linear_model import LogisticRegressionCV
from sklearn.preprocessing import StandardScaler
target = 'Great'
features = ['Yelp', 'Google', 'Cost', 'Hunger', 'Tortilla', 'Temp', 'Meat',
'Fillings', 'Meat:filling', 'Uniformity', 'Salsa', 'Synergy', 'Wrap']
X_train ... | _____no_output_____ | MIT | module4-logistic-regression/Logistic_Regression_Assignment.ipynb | afroman32/DS-Unit-2-Linear-Models |
Seminar: Monte-carlo tree searchIn this seminar, we'll implement a vanilla MCTS planning and use it to solve some Gym envs.But before we do that, we first need to modify gym env to allow saving and loading game states to facilitate backtracking. | from collections import namedtuple
from pickle import dumps, loads
from gym.core import Wrapper
# a container for get_result function below. Works just like tuple, but prettier
ActionResult = namedtuple(
"action_result", ("snapshot", "observation", "reward", "is_done", "info"))
class WithSnapshots(Wrapper):
... | _____no_output_____ | MIT | week6/practice_mcts.ipynb | Iramuk-ganh/practical-rl |
try out snapshots: | # make env
env = WithSnapshots(gym.make("CartPole-v0"))
env.reset()
n_actions = env.action_space.n
print("initial_state:")
plt.imshow(env.render('rgb_array'))
env.close()
# create first snapshot
snap0 = env.get_snapshot()
# play without making snapshots (faster)
while True:
is_done = env.step(env.action_space.sam... | _____no_output_____ | MIT | week6/practice_mcts.ipynb | Iramuk-ganh/practical-rl |
MCTS: Monte-Carlo tree searchIn this section, we'll implement the vanilla MCTS algorithm with UCB1-based node selection.We will start by implementing the `Node` class - a simple class that acts like MCTS node and supports some of the MCTS algorithm steps.This MCTS implementation makes some assumptions about the enviro... | assert isinstance(env,WithSnapshots)
class Node:
""" a tree node for MCTS """
#metadata:
parent = None #parent Node
value_sum = 0. #sum of state values from all visits (numerator)
times_visited = 0 #counter of visits (denominator)
def __init__(self,parent,action)... | _____no_output_____ | MIT | week6/practice_mcts.ipynb | Iramuk-ganh/practical-rl |
Main MCTS loopWith all we implemented, MCTS boils down to a trivial piece of code. | def plan_mcts(root,n_iters=10):
"""
builds tree with monte-carlo tree search for n_iters iterations
:param root: tree node to plan from
:param n_iters: how many select-expand-simulate-propagete loops to make
"""
for _ in range(n_iters):
# PUT CODE HERE
node = root.selec... | _____no_output_____ | MIT | week6/practice_mcts.ipynb | Iramuk-ganh/practical-rl |
Plan and executeIn this section, we use the MCTS implementation to find optimal policy. | env = WithSnapshots(gym.make("CartPole-v0"))
root_observation = env.reset()
root_snapshot = env.get_snapshot()
root = Root(root_snapshot, root_observation)
#plan from root:
plan_mcts(root,n_iters=1000)
from IPython.display import clear_output
from itertools import count
from gym.wrappers import Monitor
total_reward = ... | _____no_output_____ | MIT | week6/practice_mcts.ipynb | Iramuk-ganh/practical-rl |
Submit to Coursera | from submit import submit_mcts
submit_mcts(total_reward, "x@gmail.com", "xx") | Submitted to Coursera platform. See results on assignment page!
| MIT | week6/practice_mcts.ipynb | Iramuk-ganh/practical-rl |
$$\newcommand\bs[1]{\boldsymbol{1}}$$ IntroductionThis chapter is light but contains some important definitions. The identity matrix and the inverse of a matrix are concepts that will be very useful in subsequent chapters. Using these concepts, we will see how vectors and matrices can be transformed. To fully understa... | np.eye(3) # 3 rows, and 3 columns | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
When you "apply" the identity matrix to a vector using the dot product, the result is the same vector:$$\bs{I}_n\bs{x} = \bs{x}$$ Example 1$$\begin{bmatrix} 1 & 0 & 0 \\\\ 0 & 1 & 0 \\\\ 0 & 0 & 1\end{bmatrix}\times\begin{bmatrix} x_{1} \\\\ x_{2} \\\\ x_{3}\end{bmatrix}=\begin{bmatrix} 1 \times x_... | x = np.array([[2], [6], [3]])
x
x_id = np.eye(x.shape[0]).dot(x)
x_id | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
More generally, when $\bs{A}$ is an $m\times n$ matrix, it is a property of matrix multiplication that:$$I_m\bs{A} = \bs{A}I_n = \bs{A}$$ Visualizing the intuitionVectors and matrices occupy $n$-dimensional space. This precept allows us to think about linear algebra geometrically and, if we're lucky enough to be worki... | A = np.array([[3, 0, 2], [2, 0, -2], [0, 1, 1]])
A | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
Now we calculate its inverse: | A_inv = np.linalg.inv(A)
A_inv | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
We can check that $\bs{A^{-1}}$ is the inverse of $\bs{A}$ with Python: | A_bis = A_inv.dot(A)
A_bis | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
Sovling a system of linear equationsThe inverse matrix can be used to solve the equation $\bs{Ax}=\bs{b}$ by adding it to each term:$$\bs{A}^{-1}\bs{Ax}=\bs{A}^{-1}\bs{b}$$Since we know by definition that $\bs{A}^{-1}\bs{A}=\bs{I}$, we have:$$\bs{I}_n\bs{x}=\bs{A}^{-1}\bs{b}$$We saw that a vector is not changed when m... | A = np.array([[2, -1], [1, 1]])
A | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
And let's define $\bs{b}$: | b = np.array([[0], [3]]) | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
And now let's find the inverse of $\bs{A}$: | A_inv = np.linalg.inv(A)
A_inv | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
Since we saw that$$\bs{x}=\bs{A}^{-1}\bs{b}$$We have: | x = A_inv.dot(b)
x | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
This is our solution! $$\bs{x}=\begin{bmatrix} 1 \\\\ 2\end{bmatrix}$$Going back to the geometric interpretion of linear algebra, you can think of our solution vector $\bs{x}$ as containing a set of coordinates ($1, 2$). This point in a $2$-dimensional Cartesian plane is actually the intersection of the two lines... | #to draw the equation with Matplotlib, first create a vector with some x values
x = np.arange(-10, 10)
#then create some y values for each of those x values using the equation
y = 2*x
y1 = -x + 3
#then instantiate the plot figure
plt.figure()
#draw the first line
plt.plot(x, y)
#draw the second line
plt.plot(x, y1)
#s... | _____no_output_____ | Unlicense | Learn Math/3. Linear Algebra/3.3 Identity and Inverse Matrices/3.3 Identity and Inverse Matrices.ipynb | mcallistercs/learning-data-science |
ESML - accelerator: Quick DEMO | import sys, os
sys.path.append(os.path.abspath("../azure-enterprise-scale-ml/esml/common/")) # NOQA: E402
from esml import ESMLDataset, ESMLProject
p = ESMLProject() # Will search in ROOT for your copied SETTINGS folder '../../../settings', you should copy template settings from '../settings'
#p = ESMLProject(True) # ... | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
from azureml.core import Workspacefrom azureml.core.authentication import InteractiveLoginAuthenticationauth = InteractiveLoginAuthentication(tenant_id = p.tenant)ws = Workspace.get(name = p.workspace_name,subscription_id = p.subscription_id,resource_group = p.resource_group,auth=auth)ws.write_config(path=".", file_nam... | from azureml.core import Workspace
ws, config_name = p.authenticate_workspace_and_write_config()
ws = p.get_workspace_from_config()
ws.name
print("Are we in R&D state (no dataset versioning) = {}".format(p.rnd))
p.unregister_all_datasets(ws) # DEMO purpose
datastore = p.init(ws) | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
3) IN->`BRONZE->SILVER`->Gold- Create dataset from PANDAS - Save to SILVER | import pandas as pd
ds = p.DatasetByName("ds01_diabetes")
df = ds.Bronze.to_pandas_dataframe()
df.head() | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
3) BRONZE-SILVER (EDIT rows & SAVE)- Test change rows, same structure = new version (and new file added)- Note: not earlier files in folder are removed. They are needed for other "versions". - Expected: For 3 files: New version, 997 rows: 2 older files=627 + 1 new file=370- Expected (if we delete OLD files): New versi... | df_filtered = df[df.AGE > 0.015]
print(df.shape[0], df_filtered.shape[0]) | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
3a) Save `SILVER` ds01_diabetes | aml_silver = p.save_silver(p.DatasetByName("ds01_diabetes"),df_filtered)
aml_silver.name | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
COMPARE `BRONZE vs SILVER`- Compare and validate the feature engineering | ds01 = p.DatasetByName("ds01_diabetes")
bronze_rows = ds01.Bronze.to_pandas_dataframe().shape[0]
silver_rows = ds01.Silver.to_pandas_dataframe().shape[0]
print("Bronze: {}".format(bronze_rows)) # Expected 442 rows
print("Silver: {}".format(silver_rows)) # Expected 185 rows (filtered)
assert bronze_rows == 442,"BRONZE... | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
3b) Save `BRONZE → SILVER` ds02_other | df_edited = p.DatasetByName("ds02_other").Silver.to_pandas_dataframe()
ds02_silver = p.save_silver(p.DatasetByName("ds02_other"),df_edited)
ds02_silver.name | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
3c) Merge all `SILVERS -> then save GOLD` | df_01 = ds01.Silver.to_pandas_dataframe()
df_02 = ds02_silver.to_pandas_dataframe()
df_gold1_join = df_01.join(df_02) # left join -> NULL on df_02
print("Diabetes shape: ", df_01.shape)
print(df_gold1_join.shape) | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
Save `GOLD` v1 | print(p.rnd)
p.rnd=False # Allow versioning on DATASETS, to have lineage
ds_gold_v1 = p.save_gold(df_gold1_join) | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
3c) Ops! "faulty" GOLD - too many features | print(p.Gold.to_pandas_dataframe().shape) # 19 features...I want 11
print("Are we in RnD phase? Or do we have 'versioning on datasets=ON'")
print("RnD phase = {}".format(p.rnd)) | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
Save `GOLD` v2 | # Lets just go with features from ds01
ds_gold_v1 = p.save_gold(df_01) | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
Get `GOLD` by version | gold_1 = p.get_gold_version(1)
gold_1.to_pandas_dataframe().shape # (185, 19)
gold_2 = p.get_gold_version(2)
gold_2.to_pandas_dataframe().shape # (185, 11)
p.Gold.to_pandas_dataframe().shape # Latest version (185, 11)
df_01_filtered = df_01[df_01.AGE > 0.03807]
ds_gold_v1 = p.save_gold(df_01_filtered)
gold_2 = p.get_go... | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
TRAIN - `AutoMLFactory + ComputeFactory` | from baselayer_azure_ml import AutoMLFactory, ComputeFactory
p.dev_test_prod = "test"
print("what environment are we targeting? = {}".format(p.dev_test_prod))
automl_performance_config = p.get_automl_performance_config()
automl_performance_config
p.dev_test_prod = "dev"
automl_performance_config = p.get_automl_perfor... | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
Get `COMPUTE` for current `ENVIRONMENT` | aml_compute = p.get_training_aml_compute(ws) | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
`TRAIN` model -> See other notebook `esml_howto_2_train.ipynb` | from azureml.train.automl import AutoMLConfig
from baselayer_azure_ml import azure_metric_regression
label = "Y"
train_6, validate_set_2, test_set_2 = p.split_gold_3(0.6,label) # Auto-registerin AZURE (M03_GOLD_TRAIN | M03_GOLD_VALIDATE | M03_GOLD_TEST) # Alt: train,testv= p.Gold.random_split(percentage=0.8, seed=23)
... | _____no_output_____ | MIT | notebook_demos/esml_howto_0_short.ipynb | jostrm/azure-enterprise-scale-ml-usage |
Pymaceuticals Inc.--- Analysis* Capomulin and Ramicane showed the smallest tumor volume at the end of the study.* There appears to be a correlation between mouse weight and the average tumor volume; as weight increases, tumor volume increases.* Capomulin had the lowest IQR, indicating a more narrow spread in the resul... | # Dependencies and Setup
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
# Study data files
mouse_metadata_path = "data/Mouse_metadata.csv"
study_results_path = "data/Study_results.csv"
# Read the mouse data and the study results
mouse_metadata = pd.read_csv(mouse_metadata_path)
study_resu... | _____no_output_____ | ADSL | Pymaceuticals/pymaceuticals_basco.ipynb | bascomary/matplotlib_challenge |
Summary Statistics | # Generate a summary statistics table of mean, median, variance, standard deviation, and SEM of the tumor volume for each regimen
mean_df = clinical_trial.groupby('Drug Regimen').mean().reset_index()
mean_df = mean_df[['Drug Regimen', 'Tumor Volume (mm3)']]
mean_df = mean_df.rename(columns={'Tumor Volume (mm3)':'Mean T... | _____no_output_____ | ADSL | Pymaceuticals/pymaceuticals_basco.ipynb | bascomary/matplotlib_challenge |
Bar and Pie Charts | # Generate a bar plot showing number of data points for each treatment regimen using pandas
drug_summary.sort_values('Count', ascending=False).plot.bar(x="Drug Regimen", y="Count")
# Generate a bar plot showing number of data points for each treatment regimen using pyplot
plt.bar(drug_summary['Drug Regimen'], drug_summ... | _____no_output_____ | ADSL | Pymaceuticals/pymaceuticals_basco.ipynb | bascomary/matplotlib_challenge |
Quartiles, Outliers and Boxplots | # Calculate the final tumor volume of each mouse.
tumor_df = clinical_trial.groupby('Mouse ID').last()
tumor_df.head()
# Calculate the final tumor volume of each mouse in Capomulin treatment regime.
capomulin = tumor_df.loc[(tumor_df['Drug Regimen'] == "Capomulin"),:]
capomulin.head()
# Calculate the IQR and quantita... | _____no_output_____ | ADSL | Pymaceuticals/pymaceuticals_basco.ipynb | bascomary/matplotlib_challenge |
Line and Scatter Plots | # Generate a line plot of time point versus tumor volume for a mouse treated with Capomulin
clinical_trial.head()
single_mouse = clinical_trial[['Mouse ID', 'Timepoint', 'Tumor Volume (mm3)', 'Drug Regimen']]
single_mouse = single_mouse.loc[(single_mouse['Drug Regimen'] == "Capomulin"),:].reset_index()
single_mouse = s... | _____no_output_____ | ADSL | Pymaceuticals/pymaceuticals_basco.ipynb | bascomary/matplotlib_challenge |
Correlation and Regression | # Calculate the correlation coefficient and linear regression model
# for mouse weight and average tumor volume for the Capomulin regimen
vc_slope, vc_int, vc_r, vc_p, vc_std_err = st.linregress(weight, tumor)
vc_fit = vc_slope * weight + vc_int
plt.plot(weight,vc_fit)
weight = capomulin_test_group['Weight (g)']
tumor... | _____no_output_____ | ADSL | Pymaceuticals/pymaceuticals_basco.ipynb | bascomary/matplotlib_challenge |
In this notebook we'll provide an example for using different openrouteservice API's to help you look for an apartment. | mkdir ors-apartment
conda create -n ors-apartment python=3.6 shapely
cd ors-apartment
pip install openrouteservice ortools folium
import folium
from openrouteservice import client | _____no_output_____ | Apache-2.0 | python/Apartment_Search.ipynb | Xenovortex/ors-example |
We have just moved to San Francisco with our kids and are looking for the perfect location to get a new home. Our geo intuition tells us we have to look at the data to come to this important decision. So we decide to geek it up a bit. Apartment isochrones There are three different suggested locations for our new home.... | api_key = '' #Provide your personal API key
clnt = client.Client(key=api_key)
# Set up folium map
map1 = folium.Map(tiles='Stamen Toner', location=([37.738684, -122.450523]), zoom_start=12)
# Set up the apartment dictionary with real coordinates
apt_dict = {'first': {'location': [-122.430954, 37.792965]},
... | _____no_output_____ | Apache-2.0 | python/Apartment_Search.ipynb | Xenovortex/ors-example |
POIs around apartments For the ever-styled foodie parents we are, we need to have the 3 basic things covered: kindergarten, supermarket and hair dresser. Let's see what options we got around our apartments: | # Common request parameters
params_poi = {'request': 'pois',
'sortby': 'distance'}
# POI categories according to
# https://github.com/GIScience/openrouteservice-docs#places-response
categories_poi = {'kindergarten': [153],
'supermarket': [518],
'hairdresser': [395]}
... |
first apartment
kindergarten: 1
supermarket: 8
hairdresser: 10
second apartment
kindergarten: 3
supermarket: 1
hairdresser: 4
third apartment
kindergarten: 1
supermarket: 3
hairdresser: 2
| Apache-2.0 | python/Apartment_Search.ipynb | Xenovortex/ors-example |
So, all apartments meet all requirements. Seems like we have to drill down further. Routing from apartments to POIs To decide on a place, we would like to know from which apartment we can reach all required POI categories the quickest. So, first we look at the distances from each apartment to the respective POIs. | # Set up common request parameters
params_route = {'profile': 'foot-walking',
'format_out': 'geojson',
'geometry': 'true',
'geometry_format': 'geojson',
'instructions': 'false',
}
# Set up dict for font-awesome
style_dict = {'kindergarten': 'ch... | _____no_output_____ | Apache-2.0 | python/Apartment_Search.ipynb | Xenovortex/ors-example |
Quickest route to all POIs Now, we only need to determine which apartment is closest to all POI categories. | # Sum up the closest POIs to each apartment
for name, apt in apt_dict.items():
apt['shortest_sum'] = sum([min(cat['durations']) for cat in apt['categories'].values()])
print("{} apartments: {} mins".format(name,
apt['shortest_sum']/60
... | first apartments: 37.09 mins
second apartments: 40.325 mins
third apartments: 35.315000000000005 mins
| Apache-2.0 | python/Apartment_Search.ipynb | Xenovortex/ors-example |
Making El Nino AnimationsEl Nino is the warm phase of __[El Niño–Southern Oscillation (ENSO)](https://en.wikipedia.org/wiki/El_Ni%C3%B1o%E2%80%93Southern_Oscillation)__. It is a part of a routine climate pattern that occurs when sea surface temperatures in the tropical Pacific Ocean rise to above-normal levels for an ... | import os
from dh_py_access import package_api
import dh_py_access.lib.datahub as datahub
import xarray as xr
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
import imageio
import shutil
import datetime
import matplotlib as mpl
mpl.rcParams['font.family'] = 'Avenir Lt Std'
mp... | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
Please put your datahub API key into a file called APIKEY and place it to the notebook folder or assign your API key directly to the variable API_key! | API_key = open('APIKEY').read().strip()
server='api.planetos.com/'
version = 'v1' | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
This is a part where you should change the time period if you want to get animation of different time frame. Strongest __[El Nino years](http://ggweather.com/enso/oni.htm)__ have been 1982-83, 1997-98 and 2015-16. However, El Nino have occured more frequently. NOAA OISST dataset in Planet OS Datahub starts from 2008, ... | time_start = '2016-01-01T00:00:00'
time_end = '2016-03-10T00:00:00'
dataset_key = 'noaa_oisst_daily_1_4'
variable = 'anom'
area = 'pacific'
latitude_north = 40; latitude_south = -40
longitude_west = -180; longitude_east = -77
anim_name = variable + '_animation_' + str(datetime.datetime.strptime(time_start,'%Y-%m-%dT%H... | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
Download the data with package API- Create package objects- Send commands for the package creation- Download the package files | dh=datahub.datahub(server,version,API_key)
package = package_api.package_api(dh,dataset_key,variable,longitude_west,longitude_east,latitude_south,latitude_north,time_start,time_end,area_name=area)
package.make_package()
package.download_package() | Package exists
| MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
Here we are using xarray to read in the data. We will also rewrite longitude coordinates as they are from 0-360 at first, but Basemap requires longitude -180 to 180. | dd1 = xr.open_dataset(package.local_file_name)
dd1['lon'] = ((dd1.lon+180) % 360) - 180 | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
We like to use Basemap to plot data on it. Here we define the area. You can find more information and documentation about Basemap __[here](https://matplotlib.org/basemap/)__. | m = Basemap(projection='merc', lat_0 = 0, lon_0 = (longitude_east + longitude_west)/2,
resolution = 'l', area_thresh = 0.05,
llcrnrlon=longitude_west, llcrnrlat=latitude_south,
urcrnrlon=longitude_east, urcrnrlat=latitude_north)
lons,lats = np.meshgrid(dd1.lon,dd1.lat)
lonmap,latmap = m(lon... | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
Below we make local folder where we save images. These are the images we will use for animation. No worries, in the end, we will delete the folder from your system. | folder = './ani/'
if not os.path.exists(folder):
os.mkdir(folder) | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
Now it is time to make images from every time step. Let's also show first time step here: | vmin = -5; vmax = 5
for k in range(0,len(dd1[variable])):
filename = folder + 'ani_' + str(k).rjust(3,'0') + '.png'
fig=plt.figure(figsize=(12,10))
ax = fig.add_subplot(111)
pcm = m.pcolormesh(lonmap,latmap,dd1[variable][k,0].data,vmin = vmin, vmax = vmax,cmap='bwr')
m.fillcontinents(color='#58... | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
This is part where we are making animation. | files = sorted(os.listdir(folder))
fileList = []
for file in files:
if not file.startswith('.'):
complete_path = folder + file
fileList.append(complete_path)
writer = imageio.get_writer(anim_name, fps=4)
for im in fileList:
writer.append_data(imageio.imread(im))
writer.close()
print ('Animatio... | Animation is saved as anom_animation_2016.mp4 under current working directory
| MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
And finally, we will delete folder where images where saved. Now you just have animation in your working directory. | shutil.rmtree(folder) | _____no_output_____ | MIT | api-examples/El_Nino_animations.ipynb | steffenmodest/notebooks |
Task 2: Prediction using Unsupervised ML - K- Means Clustering Importing the libraries | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd | _____no_output_____ | Apache-2.0 | Task_2_Clustering.ipynb | BakkeshAS/GRIP_Task_2_Predict_Optimum_Clusters |
Importing the dataset | dataset = pd.read_csv('/content/Iris.csv')
dataset.head()
dataset['Species'].describe() | _____no_output_____ | Apache-2.0 | Task_2_Clustering.ipynb | BakkeshAS/GRIP_Task_2_Predict_Optimum_Clusters |
Determining K - number of clusters | x = dataset.iloc[:, [0, 1, 2, 3]].values
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 15):
kmeans = KMeans(n_clusters = i, init = 'k-means++',
max_iter = 300, n_init = 10, random_state = 0)
kmeans.fit(x)
wcss.append(kmeans.inertia_)
| _____no_output_____ | Apache-2.0 | Task_2_Clustering.ipynb | BakkeshAS/GRIP_Task_2_Predict_Optimum_Clusters |
Plotting the results - observe 'The elbow' | plt.figure(figsize=(16,8))
plt.style.use('ggplot')
plt.plot(range(1, 15), wcss)
plt.title('The elbow method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS') # Within cluster sum of squares
plt.show() | _____no_output_____ | Apache-2.0 | Task_2_Clustering.ipynb | BakkeshAS/GRIP_Task_2_Predict_Optimum_Clusters |
Creating the kmeans classifier with K = 3 | kmeans = KMeans(n_clusters = 3, init = 'k-means++',
max_iter = 300, n_init = 10, random_state = 0)
y_kmeans = kmeans.fit_predict(x) | _____no_output_____ | Apache-2.0 | Task_2_Clustering.ipynb | BakkeshAS/GRIP_Task_2_Predict_Optimum_Clusters |
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