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stringlengths 13
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sequencelengths 1
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stringclasses 1
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|---|---|---|---|
73067082/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
Checking for missing data
"""
missing_values_count = X_full.isnull().sum()
total_cells = np.product(X_full.shape)
total_missing = missing_values_count.sum()
percent_missing = total_missing / total_cells * 100
X_full.dropna(axis=0, subset=['target'], inplace=True)
y = X_full.target
X_full.drop(['target'], axis=1, inplace=True)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X_full.columns if X_full[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid_full[col]).issubset(set(X_train_full[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
object_nunique = list(map(lambda col: X_full[col].nunique(), object_cols))
d = dict(zip(object_cols, object_nunique))
sorted(d.items(), key=lambda x: x[1]) | code |
73067082/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
Checking for missing data
"""
missing_values_count = X_full.isnull().sum()
total_cells = np.product(X_full.shape)
total_missing = missing_values_count.sum()
percent_missing = total_missing / total_cells * 100
X_full.dropna(axis=0, subset=['target'], inplace=True)
y = X_full.target
X_full.drop(['target'], axis=1, inplace=True)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X_full.columns if X_full[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid_full[col]).issubset(set(X_train_full[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
object_nunique = list(map(lambda col: X_full[col].nunique(), object_cols))
d = dict(zip(object_cols, object_nunique))
sorted(d.items(), key=lambda x: x[1])
label_encoder_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
one_hotting_cols = list(set(object_cols) - set(label_encoder_cols))
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = label_encoder_cols + one_hotting_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = X_test_full[my_cols].copy()
X_train_prepared = X_train_full[numerical_cols].copy()
X_valid_prepared = X_valid_full[numerical_cols].copy()
X_test_prepared = X_test_full[numerical_cols].copy()
X_train_prepared = pd.concat([X_train_new_columns, X_train_prepared], axis=1)
X_valid_prepared = pd.concat([X_valid_new_columns, X_valid_prepared], axis=1)
X_test_prepared = pd.concat([X_test_new_columns, X_test_prepared], axis=1)
X_train_prepared = X_train_prepared.drop(['id'], axis=1)
X_valid_prepared = X_valid_prepared.drop(['id'], axis=1)
X_test_prepared = X_test_prepared.drop(['id'], axis=1)
xgb_gpu_model = XGBRegressor(random_state=1, n_jobs=4, n_estimators=5000, tree_method='gpu_hist', learning_rate=0.01, subsample=0.9, max_depth=5, colsample_bytree=0.5, reg_alpha=30, eval_metric='rmse')
xgb_gpu_model.fit(X_train_prepared, y_train)
predict_y_xgb = xgb_gpu_model.predict(X_valid_prepared)
mse = mean_squared_error(predict_y_xgb, y_valid, squared=False)
print('Mean Squared Error:', mse) | code |
73067082/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
Checking for missing data
"""
missing_values_count = X_full.isnull().sum()
total_cells = np.product(X_full.shape)
total_missing = missing_values_count.sum()
percent_missing = total_missing / total_cells * 100
X_full.dropna(axis=0, subset=['target'], inplace=True)
y = X_full.target
X_full.drop(['target'], axis=1, inplace=True)
X_train_full, X_valid_full, y_train, y_valid = train_test_split(X_full, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X_full.columns if X_full[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid_full[col]).issubset(set(X_train_full[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
object_nunique = list(map(lambda col: X_full[col].nunique(), object_cols))
d = dict(zip(object_cols, object_nunique))
sorted(d.items(), key=lambda x: x[1])
label_encoder_cols = [cname for cname in X_train_full.columns if X_train_full[cname].nunique() < 10 and X_train_full[cname].dtype == 'object']
one_hotting_cols = list(set(object_cols) - set(label_encoder_cols))
numerical_cols = [cname for cname in X_train_full.columns if X_train_full[cname].dtype in ['int64', 'float64']]
my_cols = label_encoder_cols + one_hotting_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = X_test_full[my_cols].copy()
X_train_prepared = X_train_full[numerical_cols].copy()
X_valid_prepared = X_valid_full[numerical_cols].copy()
X_test_prepared = X_test_full[numerical_cols].copy()
X_train_prepared = pd.concat([X_train_new_columns, X_train_prepared], axis=1)
X_valid_prepared = pd.concat([X_valid_new_columns, X_valid_prepared], axis=1)
X_test_prepared = pd.concat([X_test_new_columns, X_test_prepared], axis=1)
X_train_prepared = X_train_prepared.drop(['id'], axis=1)
X_valid_prepared = X_valid_prepared.drop(['id'], axis=1)
X_test_prepared = X_test_prepared.drop(['id'], axis=1)
X_train_prepared.head() | code |
73067082/cell_5 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
X_full = pd.read_csv('../input/30-days-of-ml/train.csv')
X_test_full = pd.read_csv('../input/30-days-of-ml/test.csv')
"""
Checking for missing data
"""
missing_values_count = X_full.isnull().sum()
print('Total missing values %d' % missing_values_count.sum())
total_cells = np.product(X_full.shape)
total_missing = missing_values_count.sum()
percent_missing = total_missing / total_cells * 100
print('Percent missing values %f' % percent_missing) | code |
17120078/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import time
import warnings
data = pd.read_pickle('../input/515k-reviews-after-preprocessing/After_filling_Nans')
df = pd.read_pickle('../input/515k-reviews-after-preprocessing/After preprocessing')
summary = np.array(df.Summary)
score = df['score'].values
import time
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
start_time = time.time()
best_params = []
parameters = {'alpha': [i for i in range(1, 100, 10)]}
acc = []
score = list(score)
for i in range(2000, 14000, 1000):
vec = CountVectorizer(max_features=i)
data = vec.fit_transform(summary)
nb = MultinomialNB()
clf = GridSearchCV(nb, parameters, cv=5)
x_train, x_test, y_train, y_test = train_test_split(data, score, test_size=0.3, random_state=42)
clf.fit(x_train, y_train)
acc.append(100.0 * sum(clf.predict(x_test)) / len(clf.predict(x_test)))
best_params.append(clf.best_params_)
vec = 0
data = 0
##Confusion matrix
def show_confusion_matrix(C,class_labels=['0','1']):
"""
C: ndarray, shape (2,2) as given by scikit-learn confusion_matrix function
class_labels: list of strings, default simply labels 0 and 1.
Draws confusion matrix with associated metrics.
"""
import matplotlib.pyplot as plt
import numpy as np
assert C.shape == (2,2), "Confusion matrix should be from binary classification only."
# true negative, false positive, false negative, true positive
tn = C[0,0]; fp = C[0,1]; fn = C[1,0]; tp = C[1,1];
NP = fn+tp # Num positive examples
NN = tn+fp # Num negative examples
N = NP+NN # Total num of examples
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
ax.imshow(C, interpolation='nearest', cmap=plt.cm.gray)
# Draw the grid boxes
ax.set_xlim(-0.5,2.5)
ax.set_ylim(2.5,-0.5)
ax.plot([-0.5,2.5],[0.5,0.5], '-k', lw=2)
ax.plot([-0.5,2.5],[1.5,1.5], '-k', lw=2)
ax.plot([0.5,0.5],[-0.5,2.5], '-k', lw=2)
ax.plot([1.5,1.5],[-0.5,2.5], '-k', lw=2)
# Set xlabels
ax.set_xlabel('Predicted Label', fontsize=16)
ax.set_xticks([0,1,2])
ax.set_xticklabels(class_labels + [''])
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
# These coordinate might require some tinkering. Ditto for y, below.
ax.xaxis.set_label_coords(0.34,1.06)
# Set ylabels
ax.set_ylabel('True Label', fontsize=16, rotation=90)
ax.set_yticklabels(class_labels + [''],rotation=90)
ax.set_yticks([0,1,2])
ax.yaxis.set_label_coords(-0.09,0.65)
# Fill in initial metrics: tp, tn, etc...
ax.text(0,0,
'True Neg: %d\n(Num Neg: %d)'%(tn,NN),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,1,
'False Neg: %d'%fn,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,0,
'False Pos: %d'%fp,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,1,
'True Pos: %d\n(Num Pos: %d)'%(tp,NP),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
# Fill in secondary metrics: accuracy, true pos rate, etc...
ax.text(2,0,
'False Pos Rate: %.2f'%(fp / (fp+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,1,
'True Pos Rate: %.2f'%(tp / (tp+fn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,2,
'Accuracy: %.2f'%((tp+tn+0.)/N),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,2,
'Neg Pre Val: %.2f'%(1-fn/(fn+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,2,
'Pos Pred Val: %.2f'%(tp/(tp+fp+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
plt.tight_layout()
plt.show()
start_time = time.time()
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
score_Log_reg = []
y_pred = clf.predict(x_test)
conf_NB = confusion_matrix(y_test, y_pred)
from sklearn.metrics import roc_curve, auc
probs = clf.predict_proba(x_test)
preds = probs[:, 1]
fpr, tpr, threshold = roc_curve(y_test, preds)
roc_auc = auc(fpr, tpr)
import matplotlib.pyplot as plt
plt.xlim([0, 1])
plt.ylim([0, 1])
a = log_loss(y_test, probs)
tn = conf_NB[0, 0]
fp = conf_NB[0, 1]
fn = conf_NB[1, 0]
tp = conf_NB[1, 1]
precision = 100 * float(tp) / (tp + fp)
recall = 100 * float(tp) / (tp + fn)
tp = conf_NB[0][0]
tn = conf_NB[1][1]
import warnings
from sklearn.linear_model import SGDClassifier
warnings.filterwarnings('ignore')
start_time = time.time()
best_params_logreg = []
parameters = {'loss': ['log'], 'penalty': ['l1', 'l2', 'elasticnet'], 'alpha': [float(i) / 10 for i in range(1, 10, 1)], 'n_jobs': [-1]}
warnings.filterwarnings('ignore')
clf = SGDClassifier()
clf = GridSearchCV(clf, parameters, cv=5)
clf.fit(x_train, y_train)
best_params_logreg.append(clf.best_params_)
print('Best parameters for Logistic Regression are:', best_params_logreg)
print('--- %s seconds ---' % (time.time() - start_time)) | code |
17120078/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import time
data = pd.read_pickle('../input/515k-reviews-after-preprocessing/After_filling_Nans')
df = pd.read_pickle('../input/515k-reviews-after-preprocessing/After preprocessing')
summary = np.array(df.Summary)
score = df['score'].values
import time
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
start_time = time.time()
best_params = []
parameters = {'alpha': [i for i in range(1, 100, 10)]}
acc = []
score = list(score)
for i in range(2000, 14000, 1000):
vec = CountVectorizer(max_features=i)
data = vec.fit_transform(summary)
nb = MultinomialNB()
clf = GridSearchCV(nb, parameters, cv=5)
x_train, x_test, y_train, y_test = train_test_split(data, score, test_size=0.3, random_state=42)
clf.fit(x_train, y_train)
acc.append(100.0 * sum(clf.predict(x_test)) / len(clf.predict(x_test)))
best_params.append(clf.best_params_)
vec = 0
data = 0
print('--- %s seconds ---' % (time.time() - start_time)) | code |
17120078/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import time
import re, nltk
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.stem import WordNetLemmatizer
from collections import Counter
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17120078/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import time
data = pd.read_pickle('../input/515k-reviews-after-preprocessing/After_filling_Nans')
df = pd.read_pickle('../input/515k-reviews-after-preprocessing/After preprocessing')
summary = np.array(df.Summary)
score = df['score'].values
import time
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
start_time = time.time()
best_params = []
parameters = {'alpha': [i for i in range(1, 100, 10)]}
acc = []
score = list(score)
for i in range(2000, 14000, 1000):
vec = CountVectorizer(max_features=i)
data = vec.fit_transform(summary)
nb = MultinomialNB()
clf = GridSearchCV(nb, parameters, cv=5)
x_train, x_test, y_train, y_test = train_test_split(data, score, test_size=0.3, random_state=42)
clf.fit(x_train, y_train)
acc.append(100.0 * sum(clf.predict(x_test)) / len(clf.predict(x_test)))
best_params.append(clf.best_params_)
vec = 0
data = 0
##Confusion matrix
def show_confusion_matrix(C,class_labels=['0','1']):
"""
C: ndarray, shape (2,2) as given by scikit-learn confusion_matrix function
class_labels: list of strings, default simply labels 0 and 1.
Draws confusion matrix with associated metrics.
"""
import matplotlib.pyplot as plt
import numpy as np
assert C.shape == (2,2), "Confusion matrix should be from binary classification only."
# true negative, false positive, false negative, true positive
tn = C[0,0]; fp = C[0,1]; fn = C[1,0]; tp = C[1,1];
NP = fn+tp # Num positive examples
NN = tn+fp # Num negative examples
N = NP+NN # Total num of examples
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
ax.imshow(C, interpolation='nearest', cmap=plt.cm.gray)
# Draw the grid boxes
ax.set_xlim(-0.5,2.5)
ax.set_ylim(2.5,-0.5)
ax.plot([-0.5,2.5],[0.5,0.5], '-k', lw=2)
ax.plot([-0.5,2.5],[1.5,1.5], '-k', lw=2)
ax.plot([0.5,0.5],[-0.5,2.5], '-k', lw=2)
ax.plot([1.5,1.5],[-0.5,2.5], '-k', lw=2)
# Set xlabels
ax.set_xlabel('Predicted Label', fontsize=16)
ax.set_xticks([0,1,2])
ax.set_xticklabels(class_labels + [''])
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
# These coordinate might require some tinkering. Ditto for y, below.
ax.xaxis.set_label_coords(0.34,1.06)
# Set ylabels
ax.set_ylabel('True Label', fontsize=16, rotation=90)
ax.set_yticklabels(class_labels + [''],rotation=90)
ax.set_yticks([0,1,2])
ax.yaxis.set_label_coords(-0.09,0.65)
# Fill in initial metrics: tp, tn, etc...
ax.text(0,0,
'True Neg: %d\n(Num Neg: %d)'%(tn,NN),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,1,
'False Neg: %d'%fn,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,0,
'False Pos: %d'%fp,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,1,
'True Pos: %d\n(Num Pos: %d)'%(tp,NP),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
# Fill in secondary metrics: accuracy, true pos rate, etc...
ax.text(2,0,
'False Pos Rate: %.2f'%(fp / (fp+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,1,
'True Pos Rate: %.2f'%(tp / (tp+fn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,2,
'Accuracy: %.2f'%((tp+tn+0.)/N),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,2,
'Neg Pre Val: %.2f'%(1-fn/(fn+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,2,
'Pos Pred Val: %.2f'%(tp/(tp+fp+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
plt.tight_layout()
plt.show()
start_time = time.time()
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
score_Log_reg = []
y_pred = clf.predict(x_test)
conf_NB = confusion_matrix(y_test, y_pred)
print('Confusion matrix:\n', conf_NB)
from sklearn.metrics import roc_curve, auc
probs = clf.predict_proba(x_test)
preds = probs[:, 1]
fpr, tpr, threshold = roc_curve(y_test, preds)
roc_auc = auc(fpr, tpr)
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % roc_auc)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
a = log_loss(y_test, probs)
print('The log loss for the Naive bayes is:', a)
show_confusion_matrix(conf_NB, ['Negative', 'Positive'])
tn = conf_NB[0, 0]
fp = conf_NB[0, 1]
fn = conf_NB[1, 0]
tp = conf_NB[1, 1]
precision = 100 * float(tp) / (tp + fp)
recall = 100 * float(tp) / (tp + fn)
print('Precision :', precision)
print('Recall :', recall)
tp = conf_NB[0][0]
tn = conf_NB[1][1]
print('The accuracy is {} %'.format(round(100.0 * (tp + tn) / len(y_test), 2)))
print('------------ %s seconds ------------' % (time.time() - start_time)) | code |
17120078/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import time
import warnings
data = pd.read_pickle('../input/515k-reviews-after-preprocessing/After_filling_Nans')
df = pd.read_pickle('../input/515k-reviews-after-preprocessing/After preprocessing')
summary = np.array(df.Summary)
score = df['score'].values
import time
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
start_time = time.time()
best_params = []
parameters = {'alpha': [i for i in range(1, 100, 10)]}
acc = []
score = list(score)
for i in range(2000, 14000, 1000):
vec = CountVectorizer(max_features=i)
data = vec.fit_transform(summary)
nb = MultinomialNB()
clf = GridSearchCV(nb, parameters, cv=5)
x_train, x_test, y_train, y_test = train_test_split(data, score, test_size=0.3, random_state=42)
clf.fit(x_train, y_train)
acc.append(100.0 * sum(clf.predict(x_test)) / len(clf.predict(x_test)))
best_params.append(clf.best_params_)
vec = 0
data = 0
##Confusion matrix
def show_confusion_matrix(C,class_labels=['0','1']):
"""
C: ndarray, shape (2,2) as given by scikit-learn confusion_matrix function
class_labels: list of strings, default simply labels 0 and 1.
Draws confusion matrix with associated metrics.
"""
import matplotlib.pyplot as plt
import numpy as np
assert C.shape == (2,2), "Confusion matrix should be from binary classification only."
# true negative, false positive, false negative, true positive
tn = C[0,0]; fp = C[0,1]; fn = C[1,0]; tp = C[1,1];
NP = fn+tp # Num positive examples
NN = tn+fp # Num negative examples
N = NP+NN # Total num of examples
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
ax.imshow(C, interpolation='nearest', cmap=plt.cm.gray)
# Draw the grid boxes
ax.set_xlim(-0.5,2.5)
ax.set_ylim(2.5,-0.5)
ax.plot([-0.5,2.5],[0.5,0.5], '-k', lw=2)
ax.plot([-0.5,2.5],[1.5,1.5], '-k', lw=2)
ax.plot([0.5,0.5],[-0.5,2.5], '-k', lw=2)
ax.plot([1.5,1.5],[-0.5,2.5], '-k', lw=2)
# Set xlabels
ax.set_xlabel('Predicted Label', fontsize=16)
ax.set_xticks([0,1,2])
ax.set_xticklabels(class_labels + [''])
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
# These coordinate might require some tinkering. Ditto for y, below.
ax.xaxis.set_label_coords(0.34,1.06)
# Set ylabels
ax.set_ylabel('True Label', fontsize=16, rotation=90)
ax.set_yticklabels(class_labels + [''],rotation=90)
ax.set_yticks([0,1,2])
ax.yaxis.set_label_coords(-0.09,0.65)
# Fill in initial metrics: tp, tn, etc...
ax.text(0,0,
'True Neg: %d\n(Num Neg: %d)'%(tn,NN),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,1,
'False Neg: %d'%fn,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,0,
'False Pos: %d'%fp,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,1,
'True Pos: %d\n(Num Pos: %d)'%(tp,NP),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
# Fill in secondary metrics: accuracy, true pos rate, etc...
ax.text(2,0,
'False Pos Rate: %.2f'%(fp / (fp+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,1,
'True Pos Rate: %.2f'%(tp / (tp+fn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,2,
'Accuracy: %.2f'%((tp+tn+0.)/N),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,2,
'Neg Pre Val: %.2f'%(1-fn/(fn+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,2,
'Pos Pred Val: %.2f'%(tp/(tp+fp+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
plt.tight_layout()
plt.show()
start_time = time.time()
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
score_Log_reg = []
y_pred = clf.predict(x_test)
conf_NB = confusion_matrix(y_test, y_pred)
from sklearn.metrics import roc_curve, auc
probs = clf.predict_proba(x_test)
preds = probs[:, 1]
fpr, tpr, threshold = roc_curve(y_test, preds)
roc_auc = auc(fpr, tpr)
import matplotlib.pyplot as plt
plt.xlim([0, 1])
plt.ylim([0, 1])
a = log_loss(y_test, probs)
tn = conf_NB[0, 0]
fp = conf_NB[0, 1]
fn = conf_NB[1, 0]
tp = conf_NB[1, 1]
precision = 100 * float(tp) / (tp + fp)
recall = 100 * float(tp) / (tp + fn)
tp = conf_NB[0][0]
tn = conf_NB[1][1]
import warnings
from sklearn.linear_model import SGDClassifier
warnings.filterwarnings('ignore')
start_time = time.time()
best_params_logreg = []
parameters = {'loss': ['log'], 'penalty': ['l1', 'l2', 'elasticnet'], 'alpha': [float(i) / 10 for i in range(1, 10, 1)], 'n_jobs': [-1]}
warnings.filterwarnings('ignore')
clf = SGDClassifier()
clf = GridSearchCV(clf, parameters, cv=5)
clf.fit(x_train, y_train)
best_params_logreg.append(clf.best_params_)
clf = SGDClassifier(loss='log', penalty='l2', alpha=0.1, n_jobs=-1)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
conf_log_ref = confusion_matrix(y_test, y_pred)
from sklearn.metrics import roc_curve, auc
probs = clf.predict_proba(x_test)
preds = probs[:, 1]
fpr, tpr, threshold = roc_curve(y_test, preds)
roc_auc = auc(fpr, tpr)
import matplotlib.pyplot as plt
plt.xlim([0, 1])
plt.ylim([0, 1])
a = log_loss(y_test, probs)
tn = conf_log_ref[0, 0]
fp = conf_log_ref[0, 1]
fn = conf_log_ref[1, 0]
tp = conf_log_ref[1, 1]
precision = 100 * float(tp) / (tp + fp)
recall = 100 * float(tp) / (tp + fn)
tp = conf_log_ref[0][0]
tn = conf_log_ref[1][1]
start_time = time.time()
best_params_SVM = []
parameters = {'loss': ['hinge'], 'penalty': ['l1', 'l2', 'elasticnet'], 'alpha': [float(i) / 10 for i in range(1, 10, 1)], 'n_jobs': [-1]}
clf = SGDClassifier()
clf = GridSearchCV(clf, parameters, cv=5)
clf.fit(x_train, y_train)
best_params_SVM = clf.best_params_
print('Best hyperparameters for linear SVM:', best_params_SVM)
print('------{} seconds-------'.format(time.time() - start_time)) | code |
17120078/cell_14 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import time
import warnings
data = pd.read_pickle('../input/515k-reviews-after-preprocessing/After_filling_Nans')
df = pd.read_pickle('../input/515k-reviews-after-preprocessing/After preprocessing')
summary = np.array(df.Summary)
score = df['score'].values
import time
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
start_time = time.time()
best_params = []
parameters = {'alpha': [i for i in range(1, 100, 10)]}
acc = []
score = list(score)
for i in range(2000, 14000, 1000):
vec = CountVectorizer(max_features=i)
data = vec.fit_transform(summary)
nb = MultinomialNB()
clf = GridSearchCV(nb, parameters, cv=5)
x_train, x_test, y_train, y_test = train_test_split(data, score, test_size=0.3, random_state=42)
clf.fit(x_train, y_train)
acc.append(100.0 * sum(clf.predict(x_test)) / len(clf.predict(x_test)))
best_params.append(clf.best_params_)
vec = 0
data = 0
##Confusion matrix
def show_confusion_matrix(C,class_labels=['0','1']):
"""
C: ndarray, shape (2,2) as given by scikit-learn confusion_matrix function
class_labels: list of strings, default simply labels 0 and 1.
Draws confusion matrix with associated metrics.
"""
import matplotlib.pyplot as plt
import numpy as np
assert C.shape == (2,2), "Confusion matrix should be from binary classification only."
# true negative, false positive, false negative, true positive
tn = C[0,0]; fp = C[0,1]; fn = C[1,0]; tp = C[1,1];
NP = fn+tp # Num positive examples
NN = tn+fp # Num negative examples
N = NP+NN # Total num of examples
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
ax.imshow(C, interpolation='nearest', cmap=plt.cm.gray)
# Draw the grid boxes
ax.set_xlim(-0.5,2.5)
ax.set_ylim(2.5,-0.5)
ax.plot([-0.5,2.5],[0.5,0.5], '-k', lw=2)
ax.plot([-0.5,2.5],[1.5,1.5], '-k', lw=2)
ax.plot([0.5,0.5],[-0.5,2.5], '-k', lw=2)
ax.plot([1.5,1.5],[-0.5,2.5], '-k', lw=2)
# Set xlabels
ax.set_xlabel('Predicted Label', fontsize=16)
ax.set_xticks([0,1,2])
ax.set_xticklabels(class_labels + [''])
ax.xaxis.set_label_position('top')
ax.xaxis.tick_top()
# These coordinate might require some tinkering. Ditto for y, below.
ax.xaxis.set_label_coords(0.34,1.06)
# Set ylabels
ax.set_ylabel('True Label', fontsize=16, rotation=90)
ax.set_yticklabels(class_labels + [''],rotation=90)
ax.set_yticks([0,1,2])
ax.yaxis.set_label_coords(-0.09,0.65)
# Fill in initial metrics: tp, tn, etc...
ax.text(0,0,
'True Neg: %d\n(Num Neg: %d)'%(tn,NN),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,1,
'False Neg: %d'%fn,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,0,
'False Pos: %d'%fp,
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,1,
'True Pos: %d\n(Num Pos: %d)'%(tp,NP),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
# Fill in secondary metrics: accuracy, true pos rate, etc...
ax.text(2,0,
'False Pos Rate: %.2f'%(fp / (fp+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,1,
'True Pos Rate: %.2f'%(tp / (tp+fn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(2,2,
'Accuracy: %.2f'%((tp+tn+0.)/N),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(0,2,
'Neg Pre Val: %.2f'%(1-fn/(fn+tn+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
ax.text(1,2,
'Pos Pred Val: %.2f'%(tp/(tp+fp+0.)),
va='center',
ha='center',
bbox=dict(fc='w',boxstyle='round,pad=1'))
plt.tight_layout()
plt.show()
start_time = time.time()
from sklearn.metrics import confusion_matrix
from sklearn.metrics import log_loss
score_Log_reg = []
y_pred = clf.predict(x_test)
conf_NB = confusion_matrix(y_test, y_pred)
from sklearn.metrics import roc_curve, auc
probs = clf.predict_proba(x_test)
preds = probs[:, 1]
fpr, tpr, threshold = roc_curve(y_test, preds)
roc_auc = auc(fpr, tpr)
import matplotlib.pyplot as plt
plt.xlim([0, 1])
plt.ylim([0, 1])
a = log_loss(y_test, probs)
tn = conf_NB[0, 0]
fp = conf_NB[0, 1]
fn = conf_NB[1, 0]
tp = conf_NB[1, 1]
precision = 100 * float(tp) / (tp + fp)
recall = 100 * float(tp) / (tp + fn)
tp = conf_NB[0][0]
tn = conf_NB[1][1]
import warnings
from sklearn.linear_model import SGDClassifier
warnings.filterwarnings('ignore')
start_time = time.time()
best_params_logreg = []
parameters = {'loss': ['log'], 'penalty': ['l1', 'l2', 'elasticnet'], 'alpha': [float(i) / 10 for i in range(1, 10, 1)], 'n_jobs': [-1]}
warnings.filterwarnings('ignore')
clf = SGDClassifier()
clf = GridSearchCV(clf, parameters, cv=5)
clf.fit(x_train, y_train)
best_params_logreg.append(clf.best_params_)
clf = SGDClassifier(loss='log', penalty='l2', alpha=0.1, n_jobs=-1)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
conf_log_ref = confusion_matrix(y_test, y_pred)
print('Confusion matrix:\n', conf_log_ref)
from sklearn.metrics import roc_curve, auc
probs = clf.predict_proba(x_test)
preds = probs[:, 1]
fpr, tpr, threshold = roc_curve(y_test, preds)
roc_auc = auc(fpr, tpr)
import matplotlib.pyplot as plt
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % roc_auc)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
a = log_loss(y_test, probs)
print('The log loss for the Logistic regression is:', a)
show_confusion_matrix(conf_log_ref, ['Negative', 'Positive'])
tn = conf_log_ref[0, 0]
fp = conf_log_ref[0, 1]
fn = conf_log_ref[1, 0]
tp = conf_log_ref[1, 1]
precision = 100 * float(tp) / (tp + fp)
recall = 100 * float(tp) / (tp + fn)
print('Precision :', precision)
print('Recall :', recall)
tp = conf_log_ref[0][0]
tn = conf_log_ref[1][1]
print('The accuracy is {} %'.format(round(100.0 * (tp + tn) / len(y_test), 2))) | code |
18136679/cell_13 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import Word2Vec
from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc
text = []
for doc_ in tqdm(doc):
path_ = path + doc_
text_ = read_data(path_)
text += text_
text_split = []
for text_ in tqdm(text):
text_split_ = []
for i in range(len(text_)):
text_split_.append(text_[i])
text_split.append(text_split_)
model = Word2Vec(text_split, size=100, window=5, min_count=1, workers=1, iter=30)
model['公'] | code |
18136679/cell_6 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc
text = []
for doc_ in tqdm(doc):
path_ = path + doc_
text_ = read_data(path_)
print(str(len(text_)) + 'in document' + str(doc_))
text += text_ | code |
18136679/cell_2 | [
"text_plain_output_1.png"
] | !pip install python-docx | code |
18136679/cell_11 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc
text = []
for doc_ in tqdm(doc):
path_ = path + doc_
text_ = read_data(path_)
text += text_
text_split = []
for text_ in tqdm(text):
text_split_ = []
for i in range(len(text_)):
text_split_.append(text_[i])
text_split.append(text_split_)
text_split[0] | code |
18136679/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input/research_data/research_data/')) | code |
18136679/cell_7 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import jieba
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc
text = []
for doc_ in tqdm(doc):
path_ = path + doc_
text_ = read_data(path_)
text += text_
words = set([])
for text_ in tqdm(text):
words = words | set(jieba.cut(text_, cut_all=True))
words = list(words) | code |
18136679/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import docx
import jieba
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc
text = []
for doc_ in tqdm(doc):
path_ = path + doc_
text_ = read_data(path_)
text += text_
words = set([])
for text_ in tqdm(text):
words = words | set(jieba.cut(text_, cut_all=True))
words = list(words)
len(words) | code |
18136679/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc
text = []
for doc_ in tqdm(doc):
path_ = path + doc_
text_ = read_data(path_)
text += text_
length = [len(text_) for text_ in text]
max(length) | code |
18136679/cell_10 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc
text = []
for doc_ in tqdm(doc):
path_ = path + doc_
text_ = read_data(path_)
text += text_
text_split = []
for text_ in tqdm(text):
text_split_ = []
for i in range(len(text_)):
text_split_.append(text_[i])
text_split.append(text_split_) | code |
18136679/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import docx
import os
import re
import numpy as np
import pandas as pd
import os
def read_data(file_path):
text = []
none = 0
doc = docx.Document(file_path)
for para in doc.paragraphs:
content = para.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
content = cell.text
filter_ = re.compile(u'[^一-龥]')
filtered_content = filter_.sub('', content)
if len(filtered_content) > 0:
text.append(filtered_content)
else:
none += 1
return text
path = '../input/research_data/research_data/'
doc = os.listdir('../input/research_data/research_data/')
doc | code |
32070353/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us | code |
32070353/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us | code |
32070353/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us = us.T.reset_index()
us
us = us.rename(columns={'index': 'date', 225: 'confirmed'})
us
plt.figure(figsize=(18, 5))
sns.set_style('whitegrid')
sns.barplot(x='date', y='confirmed', data=us)
plt.show() | code |
32070353/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.head() | code |
32070353/cell_23 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us = us.T.reset_index()
us
us = us.rename(columns={'index': 'date', 225: 'confirmed'})
us
plt.figure(figsize=(18, 5))
sns.barplot(x='date', y='confirmed', data=us)
plt.show() | code |
32070353/cell_33 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy | code |
32070353/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns | code |
32070353/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us = us.T.reset_index()
us
us = us.rename(columns={'index': 'date', 225: 'confirmed'})
us
sns.set_style('whitegrid')
sns.set_style('whitegrid')
plt.xticks(rotation=90)
plt.figure(figsize=(18, 5))
sns.set_style('whitegrid')
sns.lineplot(x='date', y='confirmed', data=us)
plt.xticks(rotation=90)
plt.show() | code |
32070353/cell_39 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
italy
italy = italy.T
italy
italy = italy.reset_index()
italy | code |
32070353/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
italy
italy = italy.T
italy
italy = italy.reset_index()
italy
italy = italy.rename(columns={'index': 'date', 137: 'confirmed'})
italy | code |
32070353/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32070353/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us = us.T.reset_index()
us
us = us.rename(columns={'index': 'date', 225: 'confirmed'})
us | code |
32070353/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T | code |
32070353/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us = us.T.reset_index()
us | code |
32070353/cell_35 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
italy | code |
32070353/cell_31 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us = us.T.reset_index()
us
us = us.rename(columns={'index': 'date', 225: 'confirmed'})
us
sns.set_style('whitegrid')
sns.set_style('whitegrid')
plt.xticks(rotation=90)
sns.set_style('whitegrid')
plt.xticks(rotation=90)
plt.figure(figsize=(18, 5))
sns.set_style('whitegrid')
sns.scatterplot(x='date', y='confirmed', data=us)
plt.xticks(rotation=90)
plt.show() | code |
32070353/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
us = data[data['Country/Region'] == 'US']
us
us = us.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
us.T
us = us.T.reset_index()
us
us = us.rename(columns={'index': 'date', 225: 'confirmed'})
us
sns.set_style('whitegrid')
plt.figure(figsize=(18, 5))
sns.set_style('whitegrid')
sns.barplot(x='date', y='confirmed', data=us)
plt.xticks(rotation=90)
plt.show() | code |
32070353/cell_37 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/novel-corona-virus-2019-dataset/time_series_covid_19_confirmed.csv')
data.columns
italy = data[data['Country/Region'] == 'Italy']
italy
italy = italy.drop(['Province/State', 'Country/Region', 'Lat', 'Long'], axis=1)
italy
italy = italy.T
italy | code |
121150055/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df2.isnull().sum() | code |
121150055/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.isnull().sum()
df = df.replace(to_replace='[^0-9a-zA-Z ]+', value='', regex=True)
df = df.applymap(lambda s: s.lower() if isinstance(s, str) else s)
count_vectorizer = CountVectorizer()
tfidf_vectorizer = TfidfVectorizer()
X_train_count = count_vectorizer.fit_transform(df['clean_text'])
X_train_tfidf = tfidf_vectorizer.fit_transform(df['clean_text'])
X_train_combined = pd.concat([pd.DataFrame(X_train_count.toarray()), pd.DataFrame(X_train_tfidf.toarray())], axis=1)
svm_model = SVC(kernel='linear')
svm_model.fit(X_train_combined, df['Label'])
y_pred = svm_model.predict(X_train_combined)
accuracy = accuracy_score(df['Label'], y_pred)
print('Accuracy:', accuracy) | code |
121150055/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df2.head() | code |
121150055/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
121150055/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.isnull().sum() | code |
121150055/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df2.isnull().sum()
df2 = df2.replace(to_replace='[^0-9a-zA-Z ]+', value='', regex=True)
df2 | code |
121150055/cell_3 | [
"text_html_output_1.png"
] | import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet') | code |
121150055/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics import accuracy_score
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.isnull().sum()
df2.isnull().sum()
df = df.replace(to_replace='[^0-9a-zA-Z ]+', value='', regex=True)
df2 = df2.replace(to_replace='[^0-9a-zA-Z ]+', value='', regex=True)
df = df.applymap(lambda s: s.lower() if isinstance(s, str) else s)
df2 = df2.applymap(lambda s: s.lower() if isinstance(s, str) else s)
count_vectorizer = CountVectorizer()
tfidf_vectorizer = TfidfVectorizer()
X_train_count = count_vectorizer.fit_transform(df['clean_text'])
X_train_tfidf = tfidf_vectorizer.fit_transform(df['clean_text'])
X_train_combined = pd.concat([pd.DataFrame(X_train_count.toarray()), pd.DataFrame(X_train_tfidf.toarray())], axis=1)
svm_model = SVC(kernel='linear')
svm_model.fit(X_train_combined, df['Label'])
y_pred = svm_model.predict(X_train_combined)
accuracy = accuracy_score(df['Label'], y_pred)
X_test_count = count_vectorizer.transform(df2['clean_text'])
X_test_tfidf = tfidf_vectorizer.transform(df2['clean_text'])
X_test_combined = pd.concat([pd.DataFrame(X_test_count.toarray()), pd.DataFrame(X_test_tfidf.toarray())], axis=1)
y_pred = svm_model.predict(X_test_combined)
accuracy = accuracy_score(df2['Label'], y_pred)
print('Accuracy:', accuracy) | code |
121150055/cell_14 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.isnull().sum()
df = df.replace(to_replace='[^0-9a-zA-Z ]+', value='', regex=True)
df | code |
121150055/cell_22 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.svm import SVC
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.isnull().sum()
df = df.replace(to_replace='[^0-9a-zA-Z ]+', value='', regex=True)
df = df.applymap(lambda s: s.lower() if isinstance(s, str) else s)
count_vectorizer = CountVectorizer()
tfidf_vectorizer = TfidfVectorizer()
X_train_count = count_vectorizer.fit_transform(df['clean_text'])
X_train_tfidf = tfidf_vectorizer.fit_transform(df['clean_text'])
X_train_combined = pd.concat([pd.DataFrame(X_train_count.toarray()), pd.DataFrame(X_train_tfidf.toarray())], axis=1)
svm_model = SVC(kernel='linear')
svm_model.fit(X_train_combined, df['Label']) | code |
121150055/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_train.csv', encoding='cp1252')
df2 = pd.read_csv('/kaggle/input/email-classification-nlp/SMS_test.csv', encoding='cp1252')
df.head() | code |
32072941/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train.head(2) | code |
32072941/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32072941/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
test.head(2) | code |
74060349/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = True
else:
center = max_pixel - min_pixel - center
for pix_value in range(min_pixel, max_pixel):
lut_value = pix_value * slope + intercept
voi_value = ((lut_value - center) / width + 0.5) * 255.0
clamped_value = min(max(voi_value, 0), 255)
if invert:
lut[pix_value] = round(255 - clamped_value)
else:
lut[pix_value] = round(clamped_value)
return lut
from pydicom.pixel_data_handlers.util import apply_voi_lut
image = pydicom.dcmread('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00012/T1w/Image-17.dcm')
pixels = image.pixel_array
print('Min pixel value: ' + str(np.min(pixels)))
print('Max pixel value: ' + str(np.max(pixels)))
plt.figure(figsize=(6, 6))
plt.imshow(pixels, cmap='gray') | code |
74060349/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = True
else:
center = max_pixel - min_pixel - center
for pix_value in range(min_pixel, max_pixel):
lut_value = pix_value * slope + intercept
voi_value = ((lut_value - center) / width + 0.5) * 255.0
clamped_value = min(max(voi_value, 0), 255)
if invert:
lut[pix_value] = round(255 - clamped_value)
else:
lut[pix_value] = round(clamped_value)
return lut
from pydicom.pixel_data_handlers.util import apply_voi_lut
image = pydicom.dcmread('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00012/T1w/Image-17.dcm')
pixels = image.pixel_array
fig, axes = plt.subplots(nrows=1, ncols=1, sharex=False, sharey=False, figsize=(10, 4))
plt.title('Pixel Range: ' + str(np.min(pixels)) + '-' + str(np.max(pixels)))
plt.hist(pixels.ravel(), np.max(pixels), (1, np.max(pixels)))
plt.tight_layout()
plt.show() | code |
74060349/cell_10 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = True
else:
center = max_pixel - min_pixel - center
for pix_value in range(min_pixel, max_pixel):
lut_value = pix_value * slope + intercept
voi_value = ((lut_value - center) / width + 0.5) * 255.0
clamped_value = min(max(voi_value, 0), 255)
if invert:
lut[pix_value] = round(255 - clamped_value)
else:
lut[pix_value] = round(clamped_value)
return lut
def apply_lut(pixels_in, lut):
pixels_in = pixels_in.flatten()
pixels_out = [0] * len(pixels_in)
for i in range(0, len(pixels_in)):
pixel = pixels_in[i]
pixels_out[i] = int(lut[pixel])
return pixels_out
from pydicom.pixel_data_handlers.util import apply_voi_lut
image = pydicom.dcmread('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00012/T1w/Image-17.dcm')
pixels = image.pixel_array
# Plot a histogram of the raw pixel data
fig, axes = plt.subplots(nrows=1, ncols=1,sharex=False, sharey=False, figsize=(10,4))
plt.title('Pixel Range: ' + str(np.min(pixels)) + '-' + str(np.max(pixels)))
plt.hist(pixels.ravel(), np.max(pixels), (1, np.max(pixels)))
plt.tight_layout()
plt.show()
window_width_1 = np.max(image.pixel_array)
window_center_1 = window_width_1 / 2
lut = make_lut(image.pixel_array, window_width_1, window_center_1, image.PhotometricInterpretation)
image1 = np.reshape(apply_lut(pixels, lut), (pixels.shape[0], pixels.shape[1]))
window_width_2 = 450
window_center_2 = 450
lut = make_lut(image.pixel_array, window_width_2, window_center_2, image.PhotometricInterpretation)
image2 = np.reshape(apply_lut(pixels, lut), (pixels.shape[0], pixels.shape[1]))
window_width_3 = 900
window_center_3 = 90
lut = make_lut(image.pixel_array, window_width_3, window_center_3, image.PhotometricInterpretation)
image3 = np.reshape(apply_lut(pixels, lut), (pixels.shape[0], pixels.shape[1]))
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True, figsize=(12, 12))
ax = axes.ravel()
ax[0].set_title('Default Image')
ax[0].imshow(image.pixel_array, cmap='gray')
ax[1].set_title(f'Width: {window_width_1} / Center: {window_center_1}')
ax[1].imshow(image1, cmap='gray')
ax[2].set_title(f'Width: {window_width_2} / Center: {window_center_2}')
ax[2].imshow(image2, cmap='gray')
ax[3].set_title(f'Width: {window_width_3} / Center: {window_center_3}')
ax[3].imshow(image3, cmap='gray')
plt.tight_layout()
plt.show() | code |
74060349/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pydicom
def make_lut(pixels, width, center, p_i):
slope = 1.0
intercept = 0.0
min_pixel = int(np.amin(pixels))
max_pixel = int(np.amax(pixels))
lut = [0] * (max_pixel + 1)
invert = False
if p_i == 'MONOCHROME1':
invert = True
else:
center = max_pixel - min_pixel - center
for pix_value in range(min_pixel, max_pixel):
lut_value = pix_value * slope + intercept
voi_value = ((lut_value - center) / width + 0.5) * 255.0
clamped_value = min(max(voi_value, 0), 255)
if invert:
lut[pix_value] = round(255 - clamped_value)
else:
lut[pix_value] = round(clamped_value)
return lut
def apply_lut(pixels_in, lut):
pixels_in = pixels_in.flatten()
pixels_out = [0] * len(pixels_in)
for i in range(0, len(pixels_in)):
pixel = pixels_in[i]
pixels_out[i] = int(lut[pixel])
return pixels_out
from pydicom.pixel_data_handlers.util import apply_voi_lut
image = pydicom.dcmread('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00012/T1w/Image-17.dcm')
pixels = image.pixel_array
# Plot a histogram of the raw pixel data
fig, axes = plt.subplots(nrows=1, ncols=1,sharex=False, sharey=False, figsize=(10,4))
plt.title('Pixel Range: ' + str(np.min(pixels)) + '-' + str(np.max(pixels)))
plt.hist(pixels.ravel(), np.max(pixels), (1, np.max(pixels)))
plt.tight_layout()
plt.show()
window_width_1 = np.max(image.pixel_array)
window_center_1 = window_width_1 / 2
lut = make_lut(image.pixel_array, window_width_1, window_center_1, image.PhotometricInterpretation)
image1 = np.reshape(apply_lut(pixels, lut), (pixels.shape[0], pixels.shape[1]))
window_width_2 = 450
window_center_2 = 450
lut = make_lut(image.pixel_array, window_width_2, window_center_2, image.PhotometricInterpretation)
image2 = np.reshape(apply_lut(pixels, lut), (pixels.shape[0], pixels.shape[1]))
window_width_3 = 900
window_center_3 = 90
lut = make_lut(image.pixel_array, window_width_3, window_center_3, image.PhotometricInterpretation)
image3 = np.reshape(apply_lut(pixels, lut), (pixels.shape[0], pixels.shape[1]))
fig, axes = plt.subplots(nrows=2, ncols=2,sharex=True, sharey=True, figsize=(12, 12))
ax = axes.ravel()
ax[0].set_title('Default Image')
ax[0].imshow(image.pixel_array, cmap='gray')
ax[1].set_title(f'Width: {window_width_1} / Center: {window_center_1}')
ax[1].imshow(image1, cmap='gray')
ax[2].set_title(f'Width: {window_width_2} / Center: {window_center_2}')
ax[2].imshow(image2, cmap='gray')
ax[3].set_title(f'Width: {window_width_3} / Center: {window_center_3}')
ax[3].imshow(image3, cmap='gray')
plt.tight_layout()
plt.show()
image = pydicom.dcmread('../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00014/FLAIR/Image-126.dcm')
pixels = image.pixel_array
print('Min pixel value: ' + str(np.min(pixels)))
print('Max pixel value: ' + str(np.max(pixels)))
plt.figure(figsize=(6, 6))
plt.imshow(pixels, cmap='gray') | code |
104127018/cell_21 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from yellowbrick.cluster import KElbowVisualizer
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
kmeans = KMeans()
elbow = KElbowVisualizer(kmeans, k=(2, 20))
elbow.fit(model_df)
elbow.show() | code |
104127018/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
def check_df(dataframe, head=5):
print('##################### Shape #####################')
print(dataframe.shape)
print('##################### Types #####################')
print(dataframe.dtypes)
print('##################### Head #####################')
print(dataframe.head(head))
print('##################### Tail #####################')
print(dataframe.tail(head))
print('##################### is null? #####################')
print(dataframe.isnull().sum())
print('##################### Quantiles #####################')
print(dataframe.quantile([0, 0.05, 0.5, 0.95, 0.99, 1]).T)
print(dataframe.describe().T)
check_df(df) | code |
104127018/cell_34 | [
"image_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
k_means = KMeans(n_clusters=7, random_state=42).fit(model_df)
segments = k_means.labels_
segments
final_df = df[['master_id', 'order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
final_df['segment'] = segments
final_df.groupby('segment').agg({'order_num_total_ever_online': ['mean', 'min', 'max'], 'order_num_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_online': ['mean', 'min', 'max'], 'recency': ['mean', 'min', 'max'], 'tenure': ['mean', 'min', 'max', 'count']})
hc = AgglomerativeClustering(n_clusters=5)
segments = hc.fit_predict(model_df)
final_df = df[['master_id', 'order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
final_df['segment'] = segments
final_df.groupby('segment').agg({'order_num_total_ever_online': ['mean', 'min', 'max'], 'order_num_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_online': ['mean', 'min', 'max'], 'recency': ['mean', 'min', 'max'], 'tenure': ['mean', 'min', 'max', 'count']}) | code |
104127018/cell_23 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
k_means = KMeans(n_clusters=7, random_state=42).fit(model_df)
segments = k_means.labels_
segments | code |
104127018/cell_29 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
hc_complete = linkage(model_df, 'complete')
plt.figure(figsize=(7, 5))
plt.title('Dendograms')
dend = dendrogram(hc_complete, truncate_mode='lastp', p=10, show_contracted=True, leaf_font_size=10)
plt.axhline(y=1.2, color='r', linestyle='--')
plt.show() | code |
104127018/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
k_means = KMeans(n_clusters=7, random_state=42).fit(model_df)
segments = k_means.labels_
segments
final_df = df[['master_id', 'order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
final_df['segment'] = segments
final_df.groupby('segment').agg({'order_num_total_ever_online': ['mean', 'min', 'max'], 'order_num_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_online': ['mean', 'min', 'max'], 'recency': ['mean', 'min', 'max'], 'tenure': ['mean', 'min', 'max', 'count']}) | code |
104127018/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
model_df.head() | code |
104127018/cell_7 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
df.head() | code |
104127018/cell_32 | [
"text_html_output_1.png"
] | from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
k_means = KMeans(n_clusters=7, random_state=42).fit(model_df)
segments = k_means.labels_
segments
final_df = df[['master_id', 'order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
final_df['segment'] = segments
final_df.groupby('segment').agg({'order_num_total_ever_online': ['mean', 'min', 'max'], 'order_num_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_offline': ['mean', 'min', 'max'], 'customer_value_total_ever_online': ['mean', 'min', 'max'], 'recency': ['mean', 'min', 'max'], 'tenure': ['mean', 'min', 'max', 'count']})
hc = AgglomerativeClustering(n_clusters=5)
segments = hc.fit_predict(model_df)
final_df = df[['master_id', 'order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
final_df['segment'] = segments
final_df.head() | code |
104127018/cell_16 | [
"text_plain_output_1.png"
] | """#SKEWNESS
def check_skew(df_skew, column):
skew = stats.skew(df_skew[column])
skewtest = stats.skewtest(df_skew[column])
plt.title('Distribution of ' + column)
sns.histplot(df_skew[column],color = "g")
print("{}'s: Skew: {}, : {}".format(column, skew, skewtest))
return
plt.figure(figsize=(9, 9))
plt.subplot(6, 1, 1)
check_skew(model_df,'order_num_total_ever_online')
plt.subplot(6, 1, 2)
check_skew(model_df,'order_num_total_ever_offline')
plt.subplot(6, 1, 3)
check_skew(model_df,'customer_value_total_ever_offline')
plt.subplot(6, 1, 4)
check_skew(model_df,'customer_value_total_ever_online')
plt.subplot(6, 1, 5)
check_skew(model_df,'recency')
plt.subplot(6, 1, 6)
check_skew(model_df,'tenure')
plt.tight_layout()
plt.savefig('before_transform.png', format='png', dpi=1000)
plt.show()
""" | code |
104127018/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
model_df.head() | code |
104127018/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df['order_num_total_ever_online'] = np.log1p(model_df['order_num_total_ever_online'])
model_df['order_num_total_ever_offline'] = np.log1p(model_df['order_num_total_ever_offline'])
model_df['customer_value_total_ever_offline'] = np.log1p(model_df['customer_value_total_ever_offline'])
model_df['customer_value_total_ever_online'] = np.log1p(model_df['customer_value_total_ever_online'])
model_df['recency'] = np.log1p(model_df['recency'])
model_df['tenure'] = np.log1p(model_df['tenure'])
sc = MinMaxScaler((0, 1))
model_scaling = sc.fit_transform(model_df)
model_df = pd.DataFrame(model_scaling, columns=model_df.columns)
k_means = KMeans(n_clusters=7, random_state=42).fit(model_df)
segments = k_means.labels_
segments
final_df = df[['master_id', 'order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
final_df['segment'] = segments
final_df.head() | code |
104127018/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
model_df = df[['order_num_total_ever_online', 'order_num_total_ever_offline', 'customer_value_total_ever_offline', 'customer_value_total_ever_online', 'recency', 'tenure']]
model_df.head() | code |
104127018/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
from scipy import stats
import datetime as dt
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from scipy.cluster.hierarchy import dendrogram
from scipy.cluster.hierarchy import linkage
from yellowbrick.cluster import KElbowVisualizer
from sklearn.cluster import AgglomerativeClustering
import seaborn as sns
import numpy as np
import sys
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.float_format', lambda x: '%.2f' % x)
pd.set_option('display.width', 1000)
df_ = pd.read_csv('../input/data20k/flo_data_20k.csv')
df = df_.copy()
date_columns = df.columns[df.columns.str.contains('date')]
df[date_columns] = df[date_columns].apply(pd.to_datetime)
df.info() | code |
104114369/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.read_csv('data/imdb/train.csv').sample(5)
pd.read_csv('data/imdb/valid.csv').sample(5) | code |
104114369/cell_2 | [
"text_html_output_1.png"
] | !pip install 'lightning-flash[text]' -q | code |
104114369/cell_11 | [
"text_plain_output_1.png"
] | from flash.text import TextClassificationData, TextClassifier
import flash
import torch
datamodule = TextClassificationData.from_csv('review', 'sentiment', train_file='data/imdb/train.csv', val_file='data/imdb/valid.csv', batch_size=4)
model = TextClassifier(backbone='gchhablani/bert-base-cased-finetuned-sst2', labels=datamodule.labels)
trainer = flash.Trainer(max_epochs=2, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy='freeze') | code |
104114369/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
pd.read_csv('data/imdb/train.csv').sample(5) | code |
104114369/cell_3 | [
"text_html_output_1.png"
] | !pip install 'lightning-flash[serve]' -q | code |
104114369/cell_17 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from flash.text import TextClassificationData, TextClassifier
import flash
import torch
datamodule = TextClassificationData.from_csv('review', 'sentiment', train_file='data/imdb/train.csv', val_file='data/imdb/valid.csv', batch_size=4)
model = TextClassifier(backbone='gchhablani/bert-base-cased-finetuned-sst2', labels=datamodule.labels)
trainer = flash.Trainer(max_epochs=2, gpus=torch.cuda.device_count())
trainer.finetune(model, datamodule=datamodule, strategy='freeze')
datamodule = TextClassificationData.from_lists(predict_data=["Joker's performance was outstanding!!!", 'This is the best movie ever!!!', 'This movie was terrible what a waste of time'], batch_size=10)
reloaded_model = TextClassifier.load_from_checkpoint('text_classification_model.pt')
flash.Trainer().predict(reloaded_model, datamodule=datamodule, output='labels') | code |
104114369/cell_10 | [
"text_plain_output_1.png"
] | from flash.text import TextClassificationData, TextClassifier
datamodule = TextClassificationData.from_csv('review', 'sentiment', train_file='data/imdb/train.csv', val_file='data/imdb/valid.csv', batch_size=4)
model = TextClassifier(backbone='gchhablani/bert-base-cased-finetuned-sst2', labels=datamodule.labels) | code |
105212784/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | place = input('Enter the place you want to visit:')
budget = int(input('Enter your budget:'))
if place == 'sea':
print('we can go')
if budget >= 3000:
print('hurry up, tickets are ready') | code |
48162853/cell_13 | [
"text_plain_output_1.png"
] | from datetime import datetime
from sklearn.linear_model import LinearRegression, Lasso
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):
pass
TRAIN_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/train.csv'
TEST_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/test.csv'
class DataPreprocessing:
"""Подготовка исходных данных"""
def __init__(self):
"""Параметры класса"""
self.medians = None
self.kitchen_square_quantile = None
def fit(self, X):
"""Сохранение статистик"""
self.medians = X.median()
self.kitchen_square_quantile = X['KitchenSquare'].quantile(0.975)
def transform(self, X):
"""Трансформация данных"""
X['Rooms_outlier'] = 0
X.loc[(X['Rooms'] == 0) | (X['Rooms'] >= 6), 'Rooms_outlier'] = 1
X.loc[X['Rooms'] == 0, 'Rooms'] = 1
X.loc[X['Rooms'] >= 6, 'Rooms'] = self.medians['Rooms']
condition = X['KitchenSquare'].isna() | (X['KitchenSquare'] > 16)
X.loc[condition, 'KitchenSquare'] = self.medians['KitchenSquare']
X.loc[X['KitchenSquare'] < 6, 'KitchenSquare'] = 6
X['HouseFloor_outlier'] = 0
X.loc[X['HouseFloor'] == 0, 'HouseFloor_outlier'] = 1
X.loc[X['Floor'] > X['HouseFloor'], 'HouseFloor_outlier'] = 1
X.loc[X['HouseFloor'] == 0, 'HouseFloor'] = self.medians['HouseFloor']
floor_outliers = X.loc[X['Floor'] > X['HouseFloor']].index
X.loc[floor_outliers, 'Floor'] = X.loc[floor_outliers, 'HouseFloor'].apply(lambda x: random.randint(1, x))
current_year = datetime.now().year
X['HouseYear_outlier'] = 0
X.loc[X['HouseYear'] > current_year, 'HouseYear_outlier'] = 1
X.loc[X['HouseYear'] > current_year, 'HouseYear'] = current_year
if 'Healthcare_1' in X.columns:
X.drop('Healthcare_1', axis=1, inplace=True)
X['LifeSquare_nan'] = X['LifeSquare'].isna() * 1
condition = X['LifeSquare'].isna() & ~X['Square'].isna() & ~X['KitchenSquare'].isna()
X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 10
X.fillna(self.medians, inplace=True)
X['TestSquare'] = X['Square'] - X['LifeSquare'] - X['KitchenSquare']
condition1 = (X['TestSquare'] < 0) | (X['LifeSquare'] < 10) & (X['LifeSquare'] > 0)
X.loc[condition1, 'LifeSquare'] = X.loc[condition1, 'Square'] - X.loc[condition1, 'KitchenSquare'] - 10
if 'TestSquare' in X.columns:
X.drop('TestSquare', axis=1, inplace=True)
return X
class FeatureGenetator:
"""Генерация новых фич"""
def __init__(self):
self.floor_max = None
self.agg_table_rooms_district = None
self.agg_table_rooms = None
self.add_dummies_columns = None
def fit(self, X, y=None):
X = X.copy()
df = X[(X['LifeSquare'] > 0) & (X['LifeSquare'] < 275)]
df['min_square'] = df['Square']
df['max_square'] = df['Square']
df['avg_square'] = df['Square']
df['min_price'] = df['Price']
df['max_price'] = df['Price']
df['avg_price'] = df['Price']
if y is not None:
self.agg_table_rooms_district = df.groupby(['DistrictId', 'Rooms'], as_index=False).agg({'Price': 'sum', 'Square': 'sum'}).rename(columns={'Price': 'SumPrice', 'Square': 'SumSquare'})
self.agg_table_rooms = df.groupby(['Rooms'], as_index=False).agg({'min_square': 'min', 'min_price': 'min', 'max_square': 'max', 'max_price': 'max', 'avg_square': 'mean', 'avg_price': 'mean'})
if y is not None:
self.floor_max = df['Floor'].max()
self.house_year_max = df['HouseYear'].max()
df = self.floor_to_cat(df)
df = self.year_to_cat(df)
def transform(self, X):
X = self.floor_to_cat(X)
X = self.year_to_cat(X)
if self.agg_table_rooms_district is not None:
X = X.merge(self.agg_table_rooms_district, on=['DistrictId', 'Rooms'], how='left')
if self.agg_table_rooms is not None:
X = X.merge(self.agg_table_rooms, on=['Rooms'], how='left')
if 'Price' in X.columns:
X['PriceCoeff'] = (X['Price'] - X['min_price']) / (X['max_price'] - X['min_price'])
else:
X['PriceCoeff'] = random.random()
condition = (X['LifeSquare'] < 0) | (X['LifeSquare'] > 275)
X.loc[condition, 'Square'] = X.loc[condition, 'min_square'] + (X.loc[condition, 'max_square'] - X.loc[condition, 'min_square']) * X.loc[condition, 'PriceCoeff']
X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 10
X['base_price'] = X['SumPrice'] / X['SumSquare']
X['base_price'].fillna(1000, inplace=True)
X['SquareCoeff'] = (X['Square'] - X['min_square']) / (X['max_square'] - X['min_square'])
X = self.SquareCoeff_to_cat(X)
X = self.KitchenSquare_to_cat(X)
X = self.HouseFloor_to_cat(X)
X = self.Social_1_to_cat(X)
X = self.Social_2_to_cat(X)
X = self.Social_3_to_cat(X)
columns_old = set(X.columns.tolist())
X = pd.get_dummies(X.copy(), columns=['year_cat', 'floor_cat', 'Ecology_2', 'Ecology_3', 'Shops_2', 'SquareCoeff_cat', 'KitchenSquare_cat', 'house_floor_cat', 'Social_1_cat', 'Social_2_cat', 'Social_3_cat'])
X['Ecology_1'] = X['Ecology_1'] * X['base_price'] * X['Square']
columns_new = set(X.columns.tolist())
self.add_dummies_columns = columns_new - columns_old
for col_name in list(self.add_dummies_columns):
X[col_name] = X[col_name] * X['base_price'] * X['Square']
return X
def Social_1_to_cat(self, X):
bins = [0, 10, 25, 50, X['Social_1'].max()]
X['Social_1_cat'] = pd.cut(X['Social_1'], bins=bins, labels=False)
X['Social_1_cat'].fillna(0, inplace=True)
return X
def Social_2_to_cat(self, X):
bins = [0, 500, 1000, 5000, 10000, X['Social_2'].max()]
X['Social_2_cat'] = pd.cut(X['Social_2'], bins=bins, labels=False)
X['Social_2_cat'].fillna(0, inplace=True)
return X
def Social_3_to_cat(self, X):
bins = [0, 3, 20, 60, 100, X['Social_3'].max()]
X['Social_3_cat'] = pd.cut(X['Social_3'], bins=bins, labels=False)
X['Social_3_cat'].fillna(0, inplace=True)
return X
def KitchenSquare_to_cat(self, X):
bins = [0, 9, 12, X['KitchenSquare'].max()]
X['KitchenSquare_cat'] = pd.cut(X['KitchenSquare'], bins=bins, labels=False)
X['KitchenSquare_cat'].fillna(0, inplace=True)
return X
def SquareCoeff_to_cat(self, X):
bins = [0, 2, 4, 6, 8, 10]
X['SquareCoeff_cat'] = pd.cut(X['SquareCoeff'] * 10, bins=bins, labels=False)
X['SquareCoeff_cat'].fillna(0, inplace=True)
return X
def shops_to_cat(self, X):
bins = [0, 2, 6, 16, self.floor_max]
X['floor_cat'] = pd.cut(X['Shops_1'], bins=bins, labels=False)
X['floor_cat'].fillna(-1, inplace=True)
return X
def HouseFloor_to_cat(self, X):
bins = [0, 5, 9, 24, 35, X['HouseFloor'].max()]
X['house_floor_cat'] = pd.cut(X['HouseFloor'], bins=bins, labels=False)
X['house_floor_cat'].fillna(-1, inplace=True)
return X
def floor_to_cat(self, X):
bins = [0, 3, 5, 9, 15, X['Floor'].max()]
X['floor_cat'] = pd.cut(X['Floor'], bins=bins, labels=False)
X['floor_cat'].fillna(-1, inplace=True)
return X
def year_to_cat(self, X):
bins = [0, 1925, 1941, 1945, 1955, 1965, 1985, 1995, 2005, self.house_year_max]
X['year_cat'] = pd.cut(X['HouseYear'], bins=bins, labels=False)
X['year_cat'].fillna(-1, inplace=True)
return X
train_df = pd.read_csv(TRAIN_DATASET_PATH)
test_df = pd.read_csv(TEST_DATASET_PATH)
preprocessor = DataPreprocessing()
preprocessor.fit(train_df)
train_df = preprocessor.transform(train_df)
test_df = preprocessor.transform(test_df)
features_gen = FeatureGenetator()
features_gen.fit(train_df, train_df['Price'])
train_df = features_gen.transform(train_df)
add_dummies_columns_train = features_gen.add_dummies_columns
test_df = features_gen.transform(test_df)
add_dummies_columns_test = features_gen.add_dummies_columns
feature_names_list = list(add_dummies_columns_train) + ['Ecology_1']
target_name = 'Price'
X = train_df[feature_names_list]
y = train_df[target_name]
test_df = test_df[feature_names_list]
model = Lasso(0.05)
model.fit(X_train, y_train)
y_train_preds = model.predict(X_train)
y_test_preds = model.predict(X_test)
evaluate_preds(y_train, y_train_preds, y_test, y_test_preds)
predictions = model.predict(test_df)
predictions | code |
48162853/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression, Lasso
model = Lasso(0.05)
model.fit(X_train, y_train) | code |
48162853/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime
from sklearn.linear_model import LinearRegression, Lasso
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.model_selection import train_test_split, cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):
pass
TRAIN_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/train.csv'
TEST_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/test.csv'
class DataPreprocessing:
"""Подготовка исходных данных"""
def __init__(self):
"""Параметры класса"""
self.medians = None
self.kitchen_square_quantile = None
def fit(self, X):
"""Сохранение статистик"""
self.medians = X.median()
self.kitchen_square_quantile = X['KitchenSquare'].quantile(0.975)
def transform(self, X):
"""Трансформация данных"""
X['Rooms_outlier'] = 0
X.loc[(X['Rooms'] == 0) | (X['Rooms'] >= 6), 'Rooms_outlier'] = 1
X.loc[X['Rooms'] == 0, 'Rooms'] = 1
X.loc[X['Rooms'] >= 6, 'Rooms'] = self.medians['Rooms']
condition = X['KitchenSquare'].isna() | (X['KitchenSquare'] > 16)
X.loc[condition, 'KitchenSquare'] = self.medians['KitchenSquare']
X.loc[X['KitchenSquare'] < 6, 'KitchenSquare'] = 6
X['HouseFloor_outlier'] = 0
X.loc[X['HouseFloor'] == 0, 'HouseFloor_outlier'] = 1
X.loc[X['Floor'] > X['HouseFloor'], 'HouseFloor_outlier'] = 1
X.loc[X['HouseFloor'] == 0, 'HouseFloor'] = self.medians['HouseFloor']
floor_outliers = X.loc[X['Floor'] > X['HouseFloor']].index
X.loc[floor_outliers, 'Floor'] = X.loc[floor_outliers, 'HouseFloor'].apply(lambda x: random.randint(1, x))
current_year = datetime.now().year
X['HouseYear_outlier'] = 0
X.loc[X['HouseYear'] > current_year, 'HouseYear_outlier'] = 1
X.loc[X['HouseYear'] > current_year, 'HouseYear'] = current_year
if 'Healthcare_1' in X.columns:
X.drop('Healthcare_1', axis=1, inplace=True)
X['LifeSquare_nan'] = X['LifeSquare'].isna() * 1
condition = X['LifeSquare'].isna() & ~X['Square'].isna() & ~X['KitchenSquare'].isna()
X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 10
X.fillna(self.medians, inplace=True)
X['TestSquare'] = X['Square'] - X['LifeSquare'] - X['KitchenSquare']
condition1 = (X['TestSquare'] < 0) | (X['LifeSquare'] < 10) & (X['LifeSquare'] > 0)
X.loc[condition1, 'LifeSquare'] = X.loc[condition1, 'Square'] - X.loc[condition1, 'KitchenSquare'] - 10
if 'TestSquare' in X.columns:
X.drop('TestSquare', axis=1, inplace=True)
return X
class FeatureGenetator:
"""Генерация новых фич"""
def __init__(self):
self.floor_max = None
self.agg_table_rooms_district = None
self.agg_table_rooms = None
self.add_dummies_columns = None
def fit(self, X, y=None):
X = X.copy()
df = X[(X['LifeSquare'] > 0) & (X['LifeSquare'] < 275)]
df['min_square'] = df['Square']
df['max_square'] = df['Square']
df['avg_square'] = df['Square']
df['min_price'] = df['Price']
df['max_price'] = df['Price']
df['avg_price'] = df['Price']
if y is not None:
self.agg_table_rooms_district = df.groupby(['DistrictId', 'Rooms'], as_index=False).agg({'Price': 'sum', 'Square': 'sum'}).rename(columns={'Price': 'SumPrice', 'Square': 'SumSquare'})
self.agg_table_rooms = df.groupby(['Rooms'], as_index=False).agg({'min_square': 'min', 'min_price': 'min', 'max_square': 'max', 'max_price': 'max', 'avg_square': 'mean', 'avg_price': 'mean'})
if y is not None:
self.floor_max = df['Floor'].max()
self.house_year_max = df['HouseYear'].max()
df = self.floor_to_cat(df)
df = self.year_to_cat(df)
def transform(self, X):
X = self.floor_to_cat(X)
X = self.year_to_cat(X)
if self.agg_table_rooms_district is not None:
X = X.merge(self.agg_table_rooms_district, on=['DistrictId', 'Rooms'], how='left')
if self.agg_table_rooms is not None:
X = X.merge(self.agg_table_rooms, on=['Rooms'], how='left')
if 'Price' in X.columns:
X['PriceCoeff'] = (X['Price'] - X['min_price']) / (X['max_price'] - X['min_price'])
else:
X['PriceCoeff'] = random.random()
condition = (X['LifeSquare'] < 0) | (X['LifeSquare'] > 275)
X.loc[condition, 'Square'] = X.loc[condition, 'min_square'] + (X.loc[condition, 'max_square'] - X.loc[condition, 'min_square']) * X.loc[condition, 'PriceCoeff']
X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 10
X['base_price'] = X['SumPrice'] / X['SumSquare']
X['base_price'].fillna(1000, inplace=True)
X['SquareCoeff'] = (X['Square'] - X['min_square']) / (X['max_square'] - X['min_square'])
X = self.SquareCoeff_to_cat(X)
X = self.KitchenSquare_to_cat(X)
X = self.HouseFloor_to_cat(X)
X = self.Social_1_to_cat(X)
X = self.Social_2_to_cat(X)
X = self.Social_3_to_cat(X)
columns_old = set(X.columns.tolist())
X = pd.get_dummies(X.copy(), columns=['year_cat', 'floor_cat', 'Ecology_2', 'Ecology_3', 'Shops_2', 'SquareCoeff_cat', 'KitchenSquare_cat', 'house_floor_cat', 'Social_1_cat', 'Social_2_cat', 'Social_3_cat'])
X['Ecology_1'] = X['Ecology_1'] * X['base_price'] * X['Square']
columns_new = set(X.columns.tolist())
self.add_dummies_columns = columns_new - columns_old
for col_name in list(self.add_dummies_columns):
X[col_name] = X[col_name] * X['base_price'] * X['Square']
return X
def Social_1_to_cat(self, X):
bins = [0, 10, 25, 50, X['Social_1'].max()]
X['Social_1_cat'] = pd.cut(X['Social_1'], bins=bins, labels=False)
X['Social_1_cat'].fillna(0, inplace=True)
return X
def Social_2_to_cat(self, X):
bins = [0, 500, 1000, 5000, 10000, X['Social_2'].max()]
X['Social_2_cat'] = pd.cut(X['Social_2'], bins=bins, labels=False)
X['Social_2_cat'].fillna(0, inplace=True)
return X
def Social_3_to_cat(self, X):
bins = [0, 3, 20, 60, 100, X['Social_3'].max()]
X['Social_3_cat'] = pd.cut(X['Social_3'], bins=bins, labels=False)
X['Social_3_cat'].fillna(0, inplace=True)
return X
def KitchenSquare_to_cat(self, X):
bins = [0, 9, 12, X['KitchenSquare'].max()]
X['KitchenSquare_cat'] = pd.cut(X['KitchenSquare'], bins=bins, labels=False)
X['KitchenSquare_cat'].fillna(0, inplace=True)
return X
def SquareCoeff_to_cat(self, X):
bins = [0, 2, 4, 6, 8, 10]
X['SquareCoeff_cat'] = pd.cut(X['SquareCoeff'] * 10, bins=bins, labels=False)
X['SquareCoeff_cat'].fillna(0, inplace=True)
return X
def shops_to_cat(self, X):
bins = [0, 2, 6, 16, self.floor_max]
X['floor_cat'] = pd.cut(X['Shops_1'], bins=bins, labels=False)
X['floor_cat'].fillna(-1, inplace=True)
return X
def HouseFloor_to_cat(self, X):
bins = [0, 5, 9, 24, 35, X['HouseFloor'].max()]
X['house_floor_cat'] = pd.cut(X['HouseFloor'], bins=bins, labels=False)
X['house_floor_cat'].fillna(-1, inplace=True)
return X
def floor_to_cat(self, X):
bins = [0, 3, 5, 9, 15, X['Floor'].max()]
X['floor_cat'] = pd.cut(X['Floor'], bins=bins, labels=False)
X['floor_cat'].fillna(-1, inplace=True)
return X
def year_to_cat(self, X):
bins = [0, 1925, 1941, 1945, 1955, 1965, 1985, 1995, 2005, self.house_year_max]
X['year_cat'] = pd.cut(X['HouseYear'], bins=bins, labels=False)
X['year_cat'].fillna(-1, inplace=True)
return X
train_df = pd.read_csv(TRAIN_DATASET_PATH)
test_df = pd.read_csv(TEST_DATASET_PATH)
preprocessor = DataPreprocessing()
preprocessor.fit(train_df)
train_df = preprocessor.transform(train_df)
test_df = preprocessor.transform(test_df)
features_gen = FeatureGenetator()
features_gen.fit(train_df, train_df['Price'])
train_df = features_gen.transform(train_df)
add_dummies_columns_train = features_gen.add_dummies_columns
test_df = features_gen.transform(test_df)
add_dummies_columns_test = features_gen.add_dummies_columns
feature_names_list = list(add_dummies_columns_train) + ['Ecology_1']
target_name = 'Price'
X = train_df[feature_names_list]
y = train_df[target_name]
test_df = test_df[feature_names_list]
model = Lasso(0.05)
model.fit(X_train, y_train)
y_train_preds = model.predict(X_train)
y_test_preds = model.predict(X_test)
evaluate_preds(y_train, y_train_preds, y_test, y_test_preds)
cv_score = cross_val_score(model, X, y, scoring='r2', cv=KFold(n_splits=3, shuffle=True, random_state=34))
cv_score | code |
48162853/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression, Lasso
import matplotlib.pyplot as plt
import seaborn as sns
def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):
pass
TRAIN_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/train.csv'
TEST_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/test.csv'
model = Lasso(0.05)
model.fit(X_train, y_train)
y_train_preds = model.predict(X_train)
y_test_preds = model.predict(X_test)
evaluate_preds(y_train, y_train_preds, y_test, y_test_preds) | code |
48162853/cell_12 | [
"text_plain_output_1.png"
] | from datetime import datetime
from sklearn.linear_model import LinearRegression, Lasso
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.model_selection import train_test_split, cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import random
import seaborn as sns
def evaluate_preds(train_true_values, train_pred_values, test_true_values, test_pred_values):
pass
TRAIN_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/train.csv'
TEST_DATASET_PATH = '/kaggle/input/real-estate-price-prediction-moscow/test.csv'
class DataPreprocessing:
"""Подготовка исходных данных"""
def __init__(self):
"""Параметры класса"""
self.medians = None
self.kitchen_square_quantile = None
def fit(self, X):
"""Сохранение статистик"""
self.medians = X.median()
self.kitchen_square_quantile = X['KitchenSquare'].quantile(0.975)
def transform(self, X):
"""Трансформация данных"""
X['Rooms_outlier'] = 0
X.loc[(X['Rooms'] == 0) | (X['Rooms'] >= 6), 'Rooms_outlier'] = 1
X.loc[X['Rooms'] == 0, 'Rooms'] = 1
X.loc[X['Rooms'] >= 6, 'Rooms'] = self.medians['Rooms']
condition = X['KitchenSquare'].isna() | (X['KitchenSquare'] > 16)
X.loc[condition, 'KitchenSquare'] = self.medians['KitchenSquare']
X.loc[X['KitchenSquare'] < 6, 'KitchenSquare'] = 6
X['HouseFloor_outlier'] = 0
X.loc[X['HouseFloor'] == 0, 'HouseFloor_outlier'] = 1
X.loc[X['Floor'] > X['HouseFloor'], 'HouseFloor_outlier'] = 1
X.loc[X['HouseFloor'] == 0, 'HouseFloor'] = self.medians['HouseFloor']
floor_outliers = X.loc[X['Floor'] > X['HouseFloor']].index
X.loc[floor_outliers, 'Floor'] = X.loc[floor_outliers, 'HouseFloor'].apply(lambda x: random.randint(1, x))
current_year = datetime.now().year
X['HouseYear_outlier'] = 0
X.loc[X['HouseYear'] > current_year, 'HouseYear_outlier'] = 1
X.loc[X['HouseYear'] > current_year, 'HouseYear'] = current_year
if 'Healthcare_1' in X.columns:
X.drop('Healthcare_1', axis=1, inplace=True)
X['LifeSquare_nan'] = X['LifeSquare'].isna() * 1
condition = X['LifeSquare'].isna() & ~X['Square'].isna() & ~X['KitchenSquare'].isna()
X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 10
X.fillna(self.medians, inplace=True)
X['TestSquare'] = X['Square'] - X['LifeSquare'] - X['KitchenSquare']
condition1 = (X['TestSquare'] < 0) | (X['LifeSquare'] < 10) & (X['LifeSquare'] > 0)
X.loc[condition1, 'LifeSquare'] = X.loc[condition1, 'Square'] - X.loc[condition1, 'KitchenSquare'] - 10
if 'TestSquare' in X.columns:
X.drop('TestSquare', axis=1, inplace=True)
return X
class FeatureGenetator:
"""Генерация новых фич"""
def __init__(self):
self.floor_max = None
self.agg_table_rooms_district = None
self.agg_table_rooms = None
self.add_dummies_columns = None
def fit(self, X, y=None):
X = X.copy()
df = X[(X['LifeSquare'] > 0) & (X['LifeSquare'] < 275)]
df['min_square'] = df['Square']
df['max_square'] = df['Square']
df['avg_square'] = df['Square']
df['min_price'] = df['Price']
df['max_price'] = df['Price']
df['avg_price'] = df['Price']
if y is not None:
self.agg_table_rooms_district = df.groupby(['DistrictId', 'Rooms'], as_index=False).agg({'Price': 'sum', 'Square': 'sum'}).rename(columns={'Price': 'SumPrice', 'Square': 'SumSquare'})
self.agg_table_rooms = df.groupby(['Rooms'], as_index=False).agg({'min_square': 'min', 'min_price': 'min', 'max_square': 'max', 'max_price': 'max', 'avg_square': 'mean', 'avg_price': 'mean'})
if y is not None:
self.floor_max = df['Floor'].max()
self.house_year_max = df['HouseYear'].max()
df = self.floor_to_cat(df)
df = self.year_to_cat(df)
def transform(self, X):
X = self.floor_to_cat(X)
X = self.year_to_cat(X)
if self.agg_table_rooms_district is not None:
X = X.merge(self.agg_table_rooms_district, on=['DistrictId', 'Rooms'], how='left')
if self.agg_table_rooms is not None:
X = X.merge(self.agg_table_rooms, on=['Rooms'], how='left')
if 'Price' in X.columns:
X['PriceCoeff'] = (X['Price'] - X['min_price']) / (X['max_price'] - X['min_price'])
else:
X['PriceCoeff'] = random.random()
condition = (X['LifeSquare'] < 0) | (X['LifeSquare'] > 275)
X.loc[condition, 'Square'] = X.loc[condition, 'min_square'] + (X.loc[condition, 'max_square'] - X.loc[condition, 'min_square']) * X.loc[condition, 'PriceCoeff']
X.loc[condition, 'LifeSquare'] = X.loc[condition, 'Square'] - X.loc[condition, 'KitchenSquare'] - 10
X['base_price'] = X['SumPrice'] / X['SumSquare']
X['base_price'].fillna(1000, inplace=True)
X['SquareCoeff'] = (X['Square'] - X['min_square']) / (X['max_square'] - X['min_square'])
X = self.SquareCoeff_to_cat(X)
X = self.KitchenSquare_to_cat(X)
X = self.HouseFloor_to_cat(X)
X = self.Social_1_to_cat(X)
X = self.Social_2_to_cat(X)
X = self.Social_3_to_cat(X)
columns_old = set(X.columns.tolist())
X = pd.get_dummies(X.copy(), columns=['year_cat', 'floor_cat', 'Ecology_2', 'Ecology_3', 'Shops_2', 'SquareCoeff_cat', 'KitchenSquare_cat', 'house_floor_cat', 'Social_1_cat', 'Social_2_cat', 'Social_3_cat'])
X['Ecology_1'] = X['Ecology_1'] * X['base_price'] * X['Square']
columns_new = set(X.columns.tolist())
self.add_dummies_columns = columns_new - columns_old
for col_name in list(self.add_dummies_columns):
X[col_name] = X[col_name] * X['base_price'] * X['Square']
return X
def Social_1_to_cat(self, X):
bins = [0, 10, 25, 50, X['Social_1'].max()]
X['Social_1_cat'] = pd.cut(X['Social_1'], bins=bins, labels=False)
X['Social_1_cat'].fillna(0, inplace=True)
return X
def Social_2_to_cat(self, X):
bins = [0, 500, 1000, 5000, 10000, X['Social_2'].max()]
X['Social_2_cat'] = pd.cut(X['Social_2'], bins=bins, labels=False)
X['Social_2_cat'].fillna(0, inplace=True)
return X
def Social_3_to_cat(self, X):
bins = [0, 3, 20, 60, 100, X['Social_3'].max()]
X['Social_3_cat'] = pd.cut(X['Social_3'], bins=bins, labels=False)
X['Social_3_cat'].fillna(0, inplace=True)
return X
def KitchenSquare_to_cat(self, X):
bins = [0, 9, 12, X['KitchenSquare'].max()]
X['KitchenSquare_cat'] = pd.cut(X['KitchenSquare'], bins=bins, labels=False)
X['KitchenSquare_cat'].fillna(0, inplace=True)
return X
def SquareCoeff_to_cat(self, X):
bins = [0, 2, 4, 6, 8, 10]
X['SquareCoeff_cat'] = pd.cut(X['SquareCoeff'] * 10, bins=bins, labels=False)
X['SquareCoeff_cat'].fillna(0, inplace=True)
return X
def shops_to_cat(self, X):
bins = [0, 2, 6, 16, self.floor_max]
X['floor_cat'] = pd.cut(X['Shops_1'], bins=bins, labels=False)
X['floor_cat'].fillna(-1, inplace=True)
return X
def HouseFloor_to_cat(self, X):
bins = [0, 5, 9, 24, 35, X['HouseFloor'].max()]
X['house_floor_cat'] = pd.cut(X['HouseFloor'], bins=bins, labels=False)
X['house_floor_cat'].fillna(-1, inplace=True)
return X
def floor_to_cat(self, X):
bins = [0, 3, 5, 9, 15, X['Floor'].max()]
X['floor_cat'] = pd.cut(X['Floor'], bins=bins, labels=False)
X['floor_cat'].fillna(-1, inplace=True)
return X
def year_to_cat(self, X):
bins = [0, 1925, 1941, 1945, 1955, 1965, 1985, 1995, 2005, self.house_year_max]
X['year_cat'] = pd.cut(X['HouseYear'], bins=bins, labels=False)
X['year_cat'].fillna(-1, inplace=True)
return X
train_df = pd.read_csv(TRAIN_DATASET_PATH)
test_df = pd.read_csv(TEST_DATASET_PATH)
preprocessor = DataPreprocessing()
preprocessor.fit(train_df)
train_df = preprocessor.transform(train_df)
test_df = preprocessor.transform(test_df)
features_gen = FeatureGenetator()
features_gen.fit(train_df, train_df['Price'])
train_df = features_gen.transform(train_df)
add_dummies_columns_train = features_gen.add_dummies_columns
test_df = features_gen.transform(test_df)
add_dummies_columns_test = features_gen.add_dummies_columns
feature_names_list = list(add_dummies_columns_train) + ['Ecology_1']
target_name = 'Price'
X = train_df[feature_names_list]
y = train_df[target_name]
test_df = test_df[feature_names_list]
model = Lasso(0.05)
model.fit(X_train, y_train)
y_train_preds = model.predict(X_train)
y_test_preds = model.predict(X_test)
evaluate_preds(y_train, y_train_preds, y_test, y_test_preds)
cv_score = cross_val_score(model, X, y, scoring='r2', cv=KFold(n_splits=3, shuffle=True, random_state=34))
cv_score
cv_score.mean() | code |
129033938/cell_21 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
import cv2
import cv2
import keras
import numpy as np # linear algebra
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred
y_test = np.argmax(y_test, axis=1)
img = cv2.imread('/kaggle/input/skin-cancer/skin caner/Normal/0_0_aidai_0029.jpg')
img = cv2.resize(img, (224, 224))
img = np.expand_dims(img, axis=0)
img_class = model.predict(img, batch_size=1)
score = tf.nn.softmax(img_class[0])
score = np.argmax(score)
score | code |
129033938/cell_13 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred | code |
129033938/cell_4 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
print(C) | code |
129033938/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
import cv2
import cv2
import keras
import numpy as np # linear algebra
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred
y_test = np.argmax(y_test, axis=1)
img = cv2.imread('/kaggle/input/skin-cancer/skin caner/Normal/0_0_aidai_0029.jpg')
img = cv2.resize(img, (224, 224))
img = np.expand_dims(img, axis=0)
img_class = model.predict(img, batch_size=1)
score = tf.nn.softmax(img_class[0])
score = np.argmax(score)
label = ''
if score == 0:
label = 'Normal'
elif score == 1:
label = 'melanoma'
elif score == 2:
label = 'nevus'
elif score == 3:
label = 'pigmented benign keratosis'
label | code |
129033938/cell_6 | [
"text_html_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape | code |
129033938/cell_26 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from keras.utils import load_img, img_to_array
from mpl_toolkits.axes_grid1 import ImageGrid
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
import cv2
import cv2
import keras
import keras.utils as image
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred
y_test = np.argmax(y_test, axis=1)
img = cv2.imread('/kaggle/input/skin-cancer/skin caner/Normal/0_0_aidai_0029.jpg')
img = cv2.resize(img, (224, 224))
img = np.expand_dims(img, axis=0)
img_class = model.predict(img, batch_size=1)
score = tf.nn.softmax(img_class[0])
score = np.argmax(score)
label = ''
if score == 0:
label = 'Normal'
elif score == 1:
label = 'melanoma'
elif score == 2:
label = 'nevus'
elif score == 3:
label = 'pigmented benign keratosis'
from keras.utils import load_img, img_to_array
import keras.utils as image
def predict_image_class(image_path,true_value):
img = cv2.imread(image_path)
img = cv2.resize(img,(224,224))
img = np.expand_dims(img, axis=0)
img_class = model.predict(img, batch_size=1)
score = tf.nn.softmax(img_class[0])
score=np.argmax(score)
label=''
if score==0:
label='Normal'
elif score==1:
label='melanoma'
elif score==2:
label='nevus'
elif score==3:
label='pigmented benign keratosis'
print(
"This image most likely belongs to {}"
.format(label)
)
# for folder_name in our_folders:
fig = plt.figure(1, figsize=(10, 10))
grid = ImageGrid(fig, 111, nrows_ncols=(1, 1), axes_pad=0.05)
ax = grid[0]
img = load_img(image_path, (224, 224))
img = np.array(img.convert('RGB'))
img = image.img_to_array(img)
ax.imshow(img / 255.)
ax.text(10, 100, 'True Label: %s' % true_value.upper(), color='g', backgroundcolor='w',\
alpha=0.8, size = 20)
ax.text(10, 150, 'Predicted Label: %s' % label.upper(), color='k', backgroundcolor='w',\
alpha=0.8, size = 20)
ax.axis('off')
plt.show()
predict_image_class('/kaggle/input/skin-cancer/skin caner/pigmented benign keratosis/ISIC_0024495.jpg', 'pigmented') | code |
129033938/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
import glob
import cv2 | code |
129033938/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1) | code |
129033938/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
import cv2
import cv2
import keras
import numpy as np # linear algebra
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred
y_test = np.argmax(y_test, axis=1)
img = cv2.imread('/kaggle/input/skin-cancer/skin caner/Normal/0_0_aidai_0029.jpg')
img = cv2.resize(img, (224, 224))
img = np.expand_dims(img, axis=0)
img_class = model.predict(img, batch_size=1)
score = tf.nn.softmax(img_class[0]) | code |
129033938/cell_28 | [
"text_plain_output_1.png"
] | from IPython.display import FileLink
from IPython.display import FileLink
FileLink('cancer_detection_using_VGG19.h5') | code |
129033938/cell_15 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred
y_test = np.argmax(y_test, axis=1)
from sklearn.metrics import *
print(classification_report(y_test, y_pred)) | code |
129033938/cell_16 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred
y_test = np.argmax(y_test, axis=1)
cm = confusion_matrix(y_test, y_pred)
print(cm) | code |
129033938/cell_17 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import seaborn as sns
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
y_pred = model.predict(x_test)
y_pred = np.argmax(y_pred, axis=1)
y_pred
y_test = np.argmax(y_test, axis=1)
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='.1f') | code |
129033938/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
label_dict['Normal'] | code |
129033938/cell_12 | [
"text_plain_output_1.png"
] | from keras.optimizers import Adam
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3,vgg19
import cv2
import keras
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape
label = np.array(label)
label.shape
num_classes = 4
label = keras.utils.to_categorical(label, num_classes)
model = vgg19.VGG19(include_top=True, weights=None, input_shape=(224, 224, 3), classes=4)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.0001), metrics=['accuracy'])
acc = []
for i in range(5):
x_train, x_test, y_train, y_test = train_test_split(data, label, test_size=0.2, random_state=np.random.randint(1, 1000, 1)[0])
x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.1, random_state=np.random.randint(1, 1000, 1)[0])
model.fit(x_train, y_train, validation_data=(x_valid, y_valid), epochs=15, batch_size=16, verbose=1)
acc | code |
129033938/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import cv2
import numpy as np # linear algebra
import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
os.path.join(dirname, filename)
np.random.seed(1234)
path = '/kaggle/input/skin-cancer/skin caner'
img_list = os.listdir(path)
lables = [i for i in range(len(img_list))]
label_dict = dict()
label_dict['Normal'] = 0
label_dict['melanoma'] = 1
label_dict['nevus'] = 2
label_dict['pigmented benign keratosis'] = 3
data = []
label = []
C = 0
for cat in img_list:
C = 0
pic_list = os.path.join(path, cat)
for img in os.listdir(pic_list):
image = os.path.join(pic_list, img)
if image == '/kaggle/input/skin-cancer/skin caner/Normal/34.avif':
continue
else:
image = cv2.imread(image)
image = cv2.resize(image, (224, 224))
data.append(image)
label.append(label_dict[cat])
C += 1
data = np.array(data)
data.shape | code |
2042602/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
import numpy as np # linear algebra
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(np.sqrt(metrics.mean_squared_error(y_test, predictions))) | code |
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