path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
2042602/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
plt.scatter(y_test, predictions) | code |
2042602/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
from sklearn import metrics
from subprocess import check_output
data = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
data = data.dropna()
test = test.dropna()
data.describe() | code |
2042602/cell_2 | [
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
from sklearn import metrics
from subprocess import check_output
data = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
print(data.shape)
print(test.shape) | code |
2042602/cell_11 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(metrics.mean_absolute_error(y_test, predictions)) | code |
2042602/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
from sklearn import metrics
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
data = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
data.describe() | code |
2042602/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train) | code |
2042602/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(predictions) | code |
2042602/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from matplotlib import style
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import seaborn as sns
from sklearn import metrics
from subprocess import check_output
data = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
data = data.dropna()
test = test.dropna()
print(data.shape)
print(test.shape) | code |
2042602/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import seaborn as sns
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
sns.distplot(y_test - predictions) | code |
2042602/cell_12 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
clf = LinearRegression()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(metrics.mean_squared_error(y_test, predictions)) | code |
72109830/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_water = pd.read_csv('../input/water-potability/water_potability.csv')
dict_cl_min = {'ph': 6.5, 'Hardness': 151, 'Solids': 0, 'Chloramines': 0, 'Sulfate': 0, 'Conductivity': 0, 'Organic_carbon': 0, 'Trihalomethanes': 0, 'Turbidity': 0}
dict_cl_max = {'ph': 8.5, 'Hardness': 300, 'Solids': 1200, 'Chloramines': 4, 'Sulfate': 250, 'Conductivity': 400, 'Organic_carbon': 10, 'Trihalomethanes': 80, 'Turbidity': 5}
set_cl_primary = {'Chloramines', 'Conductivity', 'Organic_carbon', 'Trihalomethanes', 'Turbidity'}
set_cl_secondary = {'ph', 'Hardness', 'Solids', 'Sulfate'}
df_cl_filter_applied_by_col = pd.DataFrame()
for col, min_val in dict_cl_min.items():
df_cl_filter_applied_by_col[col] = df_water[col] >= min_val
for col, max_val in dict_cl_max.items():
df_cl_filter_applied_by_col[col] = df_cl_filter_applied_by_col[col] & (df_water[col] <= max_val)
df_cl_filter_applied_all = df_cl_filter_applied_by_col.all(axis=1)
print('all filters result:', df_cl_filter_applied_all.value_counts(False), sep='\r\n')
df_cl_filter_applied_primary = df_cl_filter_applied_by_col[set_cl_primary].all(axis=1)
print('primary filters result:', df_cl_filter_applied_primary.value_counts(False), sep='\r\n')
df_cl_filter_applied_secondary = df_cl_filter_applied_by_col[set_cl_secondary].all(axis=1)
print('secondary filters result:', df_cl_filter_applied_secondary.value_counts(False), sep='\r\n')
print(df_water[df_cl_filter_applied_primary]) | code |
72109830/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df_water = pd.read_csv('../input/water-potability/water_potability.csv')
df_water.describe() | code |
72109830/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df_water = pd.read_csv('../input/water-potability/water_potability.csv')
dict_cl_min = {'ph': 6.5, 'Hardness': 151, 'Solids': 0, 'Chloramines': 0, 'Sulfate': 0, 'Conductivity': 0, 'Organic_carbon': 0, 'Trihalomethanes': 0, 'Turbidity': 0}
dict_cl_max = {'ph': 8.5, 'Hardness': 300, 'Solids': 1200, 'Chloramines': 4, 'Sulfate': 250, 'Conductivity': 400, 'Organic_carbon': 10, 'Trihalomethanes': 80, 'Turbidity': 5}
set_cl_primary = {'Chloramines', 'Conductivity', 'Organic_carbon', 'Trihalomethanes', 'Turbidity'}
set_cl_secondary = {'ph', 'Hardness', 'Solids', 'Sulfate'}
df_cl_filter_applied_by_col = pd.DataFrame()
for col, min_val in dict_cl_min.items():
df_cl_filter_applied_by_col[col] = df_water[col] >= min_val
for col, max_val in dict_cl_max.items():
df_cl_filter_applied_by_col[col] = df_cl_filter_applied_by_col[col] & (df_water[col] <= max_val)
print(df_cl_filter_applied_by_col[col].value_counts())
df_cl_filter_applied_by_col.head() | code |
73080358/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
X = features.copy()
print(X.shape)
X_test = test.copy()
print(X_test.shape)
categorical_cols = [cname for cname in features.columns if features[cname].dtype == 'object']
numerical_cols = [cname for cname in features.columns if features[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols | code |
73080358/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
X = features.copy()
X_test = test.copy()
categorical_cols = [cname for cname in features.columns if features[cname].dtype == 'object']
numerical_cols = [cname for cname in features.columns if features[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
ordinal_encoder = OrdinalEncoder()
print('Before OEing: ', X.shape, X_test.shape)
X[categorical_cols] = ordinal_encoder.fit_transform(features[categorical_cols])
X_test[categorical_cols] = ordinal_encoder.transform(test[categorical_cols])
print('After OEing: ', X.shape, X_test.shape)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=17)
print(X_train.shape)
print(X_valid.shape) | code |
73080358/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
from tpot import TPOTRegressor
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
X = features.copy()
X_test = test.copy()
categorical_cols = [cname for cname in features.columns if features[cname].dtype == 'object']
numerical_cols = [cname for cname in features.columns if features[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
ordinal_encoder = OrdinalEncoder()
X[categorical_cols] = ordinal_encoder.fit_transform(features[categorical_cols])
X_test[categorical_cols] = ordinal_encoder.transform(test[categorical_cols])
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=17)
scoring_function = 'accuracy'
tpot_rgr = TPOTRegressor(scoring=scoring_function, verbosity=2, random_state=42, cv=5, n_jobs=-1)
tpot_rgr.fit(X_train, y_train)
print(tpot_rgr.score(X_valid, y_valid)) | code |
73080358/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import StandardScaler
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
X = features.copy()
X_test = test.copy()
categorical_cols = [cname for cname in features.columns if features[cname].dtype == 'object']
numerical_cols = [cname for cname in features.columns if features[cname].dtype in ['int64', 'float64']]
my_cols = categorical_cols + numerical_cols
ordinal_encoder = OrdinalEncoder()
X[categorical_cols] = ordinal_encoder.fit_transform(features[categorical_cols])
X_test[categorical_cols] = ordinal_encoder.transform(test[categorical_cols])
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=17)
print('before Scaling:', X['cont0'].mean(), X_test['cont0'].mean())
scaler = StandardScaler()
X[numerical_cols] = scaler.fit_transform(X[numerical_cols])
X_test[numerical_cols] = scaler.fit_transform(X_test[numerical_cols])
print('after Scaling:', X['cont0'].mean(), X_test['cont0'].mean()) | code |
74052792/cell_6 | [
"image_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import string
import os
from collections import Counter
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
from sklearn.pipeline import Pipeline
sns.set(style='white', context='notebook', palette='magma')
markdown_data = pd.read_csv('../input/titanic/train.csv')
final_approval_data = pd.read_csv('../input/titanic/test.csv')
passenger_id_final = final_approval_data['PassengerId']
def detect_outliers(df, n, features):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than n outliers according
to the Tukey method.
"""
outlier_indices = []
for col in features:
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > n))
return multiple_outliers
Outliers_to_drop = detect_outliers(markdown_data, 2, ['Age', 'SibSp', 'Parch', 'Fare'])
markdown_data.loc[Outliers_to_drop]
markdown_data = markdown_data.drop(Outliers_to_drop).reset_index(drop=True)
sns.heatmap(markdown_data.corr(), annot=True) | code |
74052792/cell_11 | [
"text_html_output_1.png"
] | from collections import Counter
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
import numpy as np
import pandas as pd
import seaborn as sns
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import string
import os
from collections import Counter
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve
from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder
from sklearn.pipeline import Pipeline
sns.set(style='white', context='notebook', palette='magma')
markdown_data = pd.read_csv('../input/titanic/train.csv')
final_approval_data = pd.read_csv('../input/titanic/test.csv')
passenger_id_final = final_approval_data['PassengerId']
def detect_outliers(df, n, features):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than n outliers according
to the Tukey method.
"""
outlier_indices = []
for col in features:
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > n))
return multiple_outliers
Outliers_to_drop = detect_outliers(markdown_data, 2, ['Age', 'SibSp', 'Parch', 'Fare'])
markdown_data.loc[Outliers_to_drop]
markdown_data = markdown_data.drop(Outliers_to_drop).reset_index(drop=True)
train_len = len(markdown_data)
data = pd.concat([markdown_data, final_approval_data], axis=0).reset_index(drop=True)
data.isnull().sum()
all_data = [markdown_data, final_approval_data]
for dataset in all_data:
dataset['Embarked'] = dataset['Embarked'].fillna(dataset['Embarked'].mode()[0])
dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].median())
dataset['Cabin'] = dataset['Cabin'].fillna('M')
index_NaN_age = list(dataset['Age'][dataset['Age'].isnull()].index)
for i in index_NaN_age:
age_med = dataset['Age'].median()
age_pred = dataset['Age'][(dataset['SibSp'] == dataset.iloc[i]['SibSp']) & (dataset['Pclass'] == dataset.iloc[i]['Pclass'])].median()
if not np.isnan(age_pred):
dataset.loc[i, 'Age'] = age_pred
else:
dataset.loc[i, 'Age'] = age_med
def is_fare(col):
col.loc[(col <= 20) & (col >= 100)] = 0
return col
def is_age(col):
col.loc[(col <= 20) & (col >= 50)] = 0
return col
def extract_surname(data):
families = []
for i in range(len(data)):
name = data.iloc[i]
if '(' in name:
name_no_bracket = name.split('(')[0]
else:
name_no_bracket = name
family = name_no_bracket.split(',')[0]
title = name_no_bracket.split(',')[1].strip().split(' ')[0]
for c in string.punctuation:
family = family.replace(c, '').strip()
families.append(family)
return families
all_data = [markdown_data, final_approval_data]
for dataset in all_data:
dataset['Family_size'] = dataset['SibSp'] + dataset['Parch'] + 1
dataset['Family'] = extract_surname(dataset['Name'])
dataset['Deck'] = dataset['Cabin'].map(lambda str: str[0])
dataset.loc[dataset[dataset['Deck'] == 'T'].index, 'Deck'] = 'A'
dataset['Deck'] = dataset['Deck'].replace(['A', 'B', 'C'], 'ABC')
dataset['Deck'] = dataset['Deck'].replace(['D', 'E'], 'DE')
dataset['Deck'] = dataset['Deck'].replace(['F', 'G'], 'FG')
dataset['Ticket_Frequency'] = dataset.groupby('Ticket')['Ticket'].transform('count')
dataset['1_Class'] = dataset['Pclass'].map(lambda s: 1 if s == 1 else 0)
dataset['2_Class'] = dataset['Pclass'].map(lambda s: 1 if s == 2 else 0)
dataset['3_Class'] = dataset['Pclass'].map(lambda s: 1 if s == 3 else 0)
dataset['FamilyG1'] = dataset['Family_size'].map(lambda s: 1 if s == 1 else 0)
dataset['FamilyG2'] = dataset['Family_size'].map(lambda s: 1 if (s >= 2) & (s <= 4) else 0)
dataset['FamilyG3'] = dataset['Family_size'].map(lambda s: 1 if (s >= 5) & (s <= 6) else 0)
dataset['FamilyG4'] = dataset['Family_size'].map(lambda s: 1 if s > 6 else 0)
dataset['Age'] = pd.qcut(dataset['Age'], q=10, duplicates='drop')
dataset['Fare'] = pd.qcut(dataset['Fare'], q=13, duplicates='drop')
dataset['Male'] = dataset['Sex'].map(lambda s: 1 if s == 'male' else 0)
dataset['Female'] = dataset['Sex'].map(lambda s: 1 if s == 'female' else 0)
dataset['S_embarked'] = dataset['Embarked'].map(lambda s: 1 if s == 'S' else 0)
dataset['C_embarked'] = dataset['Embarked'].map(lambda s: 1 if s == 'C' else 0)
dataset['Q_embarked'] = dataset['Embarked'].map(lambda s: 1 if s == 'Q' else 0)
dataset['Title'] = dataset['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
dataset['Title'] = dataset['Title'].replace(['Miss', 'Mrs', 'Ms', 'Mlle', 'Lady', 'Mme', 'the Countess', 'Dona'], 'Miss/Mrs/Ms')
dataset['Title'] = dataset['Title'].replace(['Dr', 'Col', 'Major', 'Jonkheer', 'Capt', 'Sir', 'Don', 'Rev'], 'Dr/Military/Noble/Clergy')
markdown_data = pd.concat([markdown_data, pd.get_dummies(markdown_data['Title'])], axis=1)
final_approval_data = pd.concat([final_approval_data, pd.get_dummies(final_approval_data['Title'])], axis=1)
markdown_data = pd.concat([markdown_data, pd.get_dummies(markdown_data['Deck'])], axis=1)
final_approval_data = pd.concat([final_approval_data, pd.get_dummies(final_approval_data['Deck'])], axis=1)
markdown_data['Age'] = LabelEncoder().fit_transform(markdown_data['Age'])
final_approval_data['Age'] = LabelEncoder().fit_transform(final_approval_data['Age'])
markdown_data['Fare'] = LabelEncoder().fit_transform(markdown_data['Fare'])
final_approval_data['Fare'] = LabelEncoder().fit_transform(final_approval_data['Fare'])
non_unique_families = [x for x in markdown_data['Family'].unique() if x in final_approval_data['Family'].unique()]
non_unique_tickets = [x for x in markdown_data['Ticket'].unique() if x in final_approval_data['Ticket'].unique()]
df_family_survival_rate = markdown_data.groupby('Family')['Survived', 'Family_size'].median()
df_ticket_survival_rate = markdown_data.groupby('Ticket')['Survived', 'Ticket_Frequency'].median()
family_rates = {}
ticket_rates = {}
for i in range(len(df_family_survival_rate)):
if df_family_survival_rate.index[i] in non_unique_families and df_family_survival_rate.iloc[i, 1] > 1:
family_rates[df_family_survival_rate.index[i]] = df_family_survival_rate.iloc[i, 0]
for i in range(len(df_ticket_survival_rate)):
if df_ticket_survival_rate.index[i] in non_unique_tickets and df_ticket_survival_rate.iloc[i, 1] > 1:
ticket_rates[df_ticket_survival_rate.index[i]] = df_ticket_survival_rate.iloc[i, 0] | code |
74052792/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
markdown_data = pd.read_csv('../input/titanic/train.csv')
final_approval_data = pd.read_csv('../input/titanic/test.csv')
passenger_id_final = final_approval_data['PassengerId']
def detect_outliers(df, n, features):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than n outliers according
to the Tukey method.
"""
outlier_indices = []
for col in features:
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > n))
return multiple_outliers
Outliers_to_drop = detect_outliers(markdown_data, 2, ['Age', 'SibSp', 'Parch', 'Fare'])
markdown_data.loc[Outliers_to_drop] | code |
74052792/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from collections import Counter
import numpy as np
import pandas as pd
markdown_data = pd.read_csv('../input/titanic/train.csv')
final_approval_data = pd.read_csv('../input/titanic/test.csv')
passenger_id_final = final_approval_data['PassengerId']
def detect_outliers(df, n, features):
"""
Takes a dataframe df of features and returns a list of the indices
corresponding to the observations containing more than n outliers according
to the Tukey method.
"""
outlier_indices = []
for col in features:
Q1 = np.percentile(df[col], 25)
Q3 = np.percentile(df[col], 75)
IQR = Q3 - Q1
outlier_step = 1.5 * IQR
outlier_list_col = df[(df[col] < Q1 - outlier_step) | (df[col] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((k for k, v in outlier_indices.items() if v > n))
return multiple_outliers
Outliers_to_drop = detect_outliers(markdown_data, 2, ['Age', 'SibSp', 'Parch', 'Fare'])
markdown_data.loc[Outliers_to_drop]
markdown_data = markdown_data.drop(Outliers_to_drop).reset_index(drop=True)
train_len = len(markdown_data)
data = pd.concat([markdown_data, final_approval_data], axis=0).reset_index(drop=True)
data.isnull().sum() | code |
50239687/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dropna()
fig = plt.figure(figsize = (20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.lineplot(data = data, x = "Age", y = "Survived", ax = ax_1)
sns.lineplot(data = data, x = "Sex", y = "Survived", ax = ax_2)
sns.lineplot(data = data, x = "Fare", y = "Survived", ax = ax_3)
sns.lineplot(data = data, x = "Pclass", y = "Survived", ax = ax_4)
sns.lineplot(data = data, x = "sibsp", y = "Survived", ax = ax_5)
sns.lineplot(data = data, x = "Parch", y = "Survived", ax = ax_6)
sns.lineplot(data = data, x = "Embarked", y = "Survived", ax = ax_7)
sns.lineplot(data = data, x = "Age", y = "Fare", ax = ax_8)
sns.lineplot(data = data, x = "Sex", y = "Fare", ax = ax_9)
plt.show()
fig = plt.figure(figsize = (20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.barplot(data = data, x = "Age", y = "Survived", ax = ax_1)
sns.barplot(data = data, x = "Sex", y = "Survived", ax = ax_2)
sns.barplot(data = data, x = "Fare", y = "Survived", ax = ax_3)
sns.barplot(data = data, x = "Pclass", y = "Survived", ax = ax_4)
sns.barplot(data = data, x = "sibsp", y = "Survived", ax = ax_5)
sns.barplot(data = data, x = "Parch", y = "Survived", ax = ax_6)
sns.barplot(data = data, x = "Embarked", y = "Survived", ax = ax_7)
sns.barplot(data = data, x = "Age", y = "Fare", ax = ax_8)
sns.barplot(data = data, x = "Sex", y = "Fare", ax = ax_9)
plt.show()
sns.pairplot(data)
plt.show() | code |
50239687/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dropna()
print(data.size // 8) | code |
50239687/cell_6 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data.head(10) | code |
50239687/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dropna()
fig = plt.figure(figsize=(20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.lineplot(data=data, x='Age', y='Survived', ax=ax_1)
sns.lineplot(data=data, x='Sex', y='Survived', ax=ax_2)
sns.lineplot(data=data, x='Fare', y='Survived', ax=ax_3)
sns.lineplot(data=data, x='Pclass', y='Survived', ax=ax_4)
sns.lineplot(data=data, x='sibsp', y='Survived', ax=ax_5)
sns.lineplot(data=data, x='Parch', y='Survived', ax=ax_6)
sns.lineplot(data=data, x='Embarked', y='Survived', ax=ax_7)
sns.lineplot(data=data, x='Age', y='Fare', ax=ax_8)
sns.lineplot(data=data, x='Sex', y='Fare', ax=ax_9)
plt.show() | code |
50239687/cell_1 | [
"image_output_1.png"
] | from IPython.display import Image
import os
from IPython.display import Image
Image(filename='../input/titlecw/title.png') | code |
50239687/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
sns.heatmap(data.isna())
plt.show() | code |
50239687/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
print(data.size // 8) | code |
50239687/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dropna()
fig = plt.figure(figsize = (20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.lineplot(data = data, x = "Age", y = "Survived", ax = ax_1)
sns.lineplot(data = data, x = "Sex", y = "Survived", ax = ax_2)
sns.lineplot(data = data, x = "Fare", y = "Survived", ax = ax_3)
sns.lineplot(data = data, x = "Pclass", y = "Survived", ax = ax_4)
sns.lineplot(data = data, x = "sibsp", y = "Survived", ax = ax_5)
sns.lineplot(data = data, x = "Parch", y = "Survived", ax = ax_6)
sns.lineplot(data = data, x = "Embarked", y = "Survived", ax = ax_7)
sns.lineplot(data = data, x = "Age", y = "Fare", ax = ax_8)
sns.lineplot(data = data, x = "Sex", y = "Fare", ax = ax_9)
plt.show()
fig = plt.figure(figsize = (20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.barplot(data = data, x = "Age", y = "Survived", ax = ax_1)
sns.barplot(data = data, x = "Sex", y = "Survived", ax = ax_2)
sns.barplot(data = data, x = "Fare", y = "Survived", ax = ax_3)
sns.barplot(data = data, x = "Pclass", y = "Survived", ax = ax_4)
sns.barplot(data = data, x = "sibsp", y = "Survived", ax = ax_5)
sns.barplot(data = data, x = "Parch", y = "Survived", ax = ax_6)
sns.barplot(data = data, x = "Embarked", y = "Survived", ax = ax_7)
sns.barplot(data = data, x = "Age", y = "Fare", ax = ax_8)
sns.barplot(data = data, x = "Sex", y = "Fare", ax = ax_9)
plt.show()
titles = ['Age', 'Fare', 'Sex', 'sibsp', 'Parch', 'Pclass', 'Embarked', 'Survived']
for i in titles:
print(f'\nExpectancy of {i} =', round(data[f'{i}'].mean(), 3))
print(f'Standard deviation of {i} =', round(data[f'{i}'].std(), 3))
print('\n') | code |
50239687/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dropna()
fig = plt.figure(figsize = (20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.lineplot(data = data, x = "Age", y = "Survived", ax = ax_1)
sns.lineplot(data = data, x = "Sex", y = "Survived", ax = ax_2)
sns.lineplot(data = data, x = "Fare", y = "Survived", ax = ax_3)
sns.lineplot(data = data, x = "Pclass", y = "Survived", ax = ax_4)
sns.lineplot(data = data, x = "sibsp", y = "Survived", ax = ax_5)
sns.lineplot(data = data, x = "Parch", y = "Survived", ax = ax_6)
sns.lineplot(data = data, x = "Embarked", y = "Survived", ax = ax_7)
sns.lineplot(data = data, x = "Age", y = "Fare", ax = ax_8)
sns.lineplot(data = data, x = "Sex", y = "Fare", ax = ax_9)
plt.show()
fig = plt.figure(figsize = (20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.barplot(data = data, x = "Age", y = "Survived", ax = ax_1)
sns.barplot(data = data, x = "Sex", y = "Survived", ax = ax_2)
sns.barplot(data = data, x = "Fare", y = "Survived", ax = ax_3)
sns.barplot(data = data, x = "Pclass", y = "Survived", ax = ax_4)
sns.barplot(data = data, x = "sibsp", y = "Survived", ax = ax_5)
sns.barplot(data = data, x = "Parch", y = "Survived", ax = ax_6)
sns.barplot(data = data, x = "Embarked", y = "Survived", ax = ax_7)
sns.barplot(data = data, x = "Age", y = "Fare", ax = ax_8)
sns.barplot(data = data, x = "Sex", y = "Fare", ax = ax_9)
plt.show()
sns.heatmap(data=data.corr(), linewidths=0.5, annot=True)
plt.show() | code |
50239687/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data = data.rename(columns={'2urvived': 'Survived'})
data = data.drop(columns=['Passengerid', 'zero'])
for i in range(1, 19):
data = data.drop(columns=f'zero.{i}')
data = data.dropna()
fig = plt.figure(figsize = (20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.lineplot(data = data, x = "Age", y = "Survived", ax = ax_1)
sns.lineplot(data = data, x = "Sex", y = "Survived", ax = ax_2)
sns.lineplot(data = data, x = "Fare", y = "Survived", ax = ax_3)
sns.lineplot(data = data, x = "Pclass", y = "Survived", ax = ax_4)
sns.lineplot(data = data, x = "sibsp", y = "Survived", ax = ax_5)
sns.lineplot(data = data, x = "Parch", y = "Survived", ax = ax_6)
sns.lineplot(data = data, x = "Embarked", y = "Survived", ax = ax_7)
sns.lineplot(data = data, x = "Age", y = "Fare", ax = ax_8)
sns.lineplot(data = data, x = "Sex", y = "Fare", ax = ax_9)
plt.show()
fig = plt.figure(figsize=(20, 20))
ax_1 = fig.add_subplot(3, 3, 1)
ax_2 = fig.add_subplot(3, 3, 2)
ax_3 = fig.add_subplot(3, 3, 3)
ax_4 = fig.add_subplot(3, 3, 4)
ax_5 = fig.add_subplot(3, 3, 5)
ax_6 = fig.add_subplot(3, 3, 6)
ax_7 = fig.add_subplot(3, 3, 7)
ax_8 = fig.add_subplot(3, 3, 8)
ax_9 = fig.add_subplot(3, 3, 9)
sns.barplot(data=data, x='Age', y='Survived', ax=ax_1)
sns.barplot(data=data, x='Sex', y='Survived', ax=ax_2)
sns.barplot(data=data, x='Fare', y='Survived', ax=ax_3)
sns.barplot(data=data, x='Pclass', y='Survived', ax=ax_4)
sns.barplot(data=data, x='sibsp', y='Survived', ax=ax_5)
sns.barplot(data=data, x='Parch', y='Survived', ax=ax_6)
sns.barplot(data=data, x='Embarked', y='Survived', ax=ax_7)
sns.barplot(data=data, x='Age', y='Fare', ax=ax_8)
sns.barplot(data=data, x='Sex', y='Fare', ax=ax_9)
plt.show() | code |
50239687/cell_5 | [
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/titanic/train_and_test2.csv')
data.head(10) | code |
122258520/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv')
census = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/census_starter.csv')
revealed_test_dates = revealed_test.first_day_of_month.unique()
train = pd.concat([train, revealed_test])
train.index = np.arange(0, len(train))
dates = train.first_day_of_month.unique()
dates
unique_cfips = list(train.cfips.unique())
def smape(y_true, y_pred):
denominator = (y_true + tf.abs(y_pred)) / 200.0
diff = tf.abs(y_true - y_pred) / denominator
diff = tf.where(denominator == 0, 0.0, diff)
return tf.reduce_mean(diff)
def cauclate_smape(item):
cfips = item.iloc[0].cfips
y_true = tf.constant(item['microbusiness_density'], dtype=tf.float64)
y_pred = tf.constant(item['prediction'], dtype=tf.float64)
return smape(y_true, y_pred).numpy()
train['prediction'] = 0
for i in range(len(dates) - 5):
date = dates[i]
df = train[train.first_day_of_month == date].copy()
last_value_dict = dict()
for j in range(len(df)):
last_value_dict[df.iloc[j].cfips] = df.iloc[j].microbusiness_density
validate_dates = dates[i + 3:i + 6]
for date in validate_dates:
df = train[train.first_day_of_month == date].copy()
train.loc[train.first_day_of_month == date, 'prediction'] = df.cfips.apply(lambda cfips: last_value_dict[cfips])
smapes = train[train.first_day_of_month.isin(validate_dates)].groupby('first_day_of_month').apply(cauclate_smape)
print(f'Last Value Date:{date} Validation Date:{validate_dates}')
print(smapes)
print('Validation Score')
print(np.mean(smapes)) | code |
122258520/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv')
census = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/census_starter.csv')
revealed_test_dates = revealed_test.first_day_of_month.unique()
train = pd.concat([train, revealed_test])
train.index = np.arange(0, len(train))
train.head() | code |
122258520/cell_10 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv')
census = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/census_starter.csv')
revealed_test_dates = revealed_test.first_day_of_month.unique()
train = pd.concat([train, revealed_test])
train.index = np.arange(0, len(train))
COLS = ['GEO_ID', 'NAME', 'S0101_C01_026E']
df2020 = pd.read_csv('/kaggle/input/census-data-for-godaddy/ACSST5Y2020.S0101-Data.csv', usecols=COLS)
df2020 = df2020.iloc[1:]
df2020['S0101_C01_026E'] = df2020['S0101_C01_026E'].astype('int')
df2021 = pd.read_csv('/kaggle/input/census-data-for-godaddy/ACSST5Y2021.S0101-Data.csv', usecols=COLS)
df2021 = df2021.iloc[1:]
df2021['S0101_C01_026E'] = df2021['S0101_C01_026E'].astype('int')
df2020['cfips'] = df2020.GEO_ID.apply(lambda x: int(x.split('US')[-1]))
adult2020 = df2020.set_index('cfips').S0101_C01_026E.to_dict()
df2021['cfips'] = df2021.GEO_ID.apply(lambda x: int(x.split('US')[-1]))
adult2021 = df2021.set_index('cfips').S0101_C01_026E.to_dict() | code |
122258520/cell_12 | [
"text_plain_output_1.png"
] | from tqdm.notebook import tqdm
import numpy as np
import pandas as pd
import tensorflow as tf
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv')
census = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/census_starter.csv')
revealed_test_dates = revealed_test.first_day_of_month.unique()
train = pd.concat([train, revealed_test])
train.index = np.arange(0, len(train))
dates = train.first_day_of_month.unique()
dates
unique_cfips = list(train.cfips.unique())
def smape(y_true, y_pred):
denominator = (y_true + tf.abs(y_pred)) / 200.0
diff = tf.abs(y_true - y_pred) / denominator
diff = tf.where(denominator == 0, 0.0, diff)
return tf.reduce_mean(diff)
def cauclate_smape(item):
cfips = item.iloc[0].cfips
y_true = tf.constant(item['microbusiness_density'], dtype=tf.float64)
y_pred = tf.constant(item['prediction'], dtype=tf.float64)
return smape(y_true, y_pred).numpy()
train['prediction'] = 0
for i in range(len(dates) - 5):
date = dates[i]
df = train[train.first_day_of_month == date].copy()
last_value_dict = dict()
for j in range(len(df)):
last_value_dict[df.iloc[j].cfips] = df.iloc[j].microbusiness_density
validate_dates = dates[i + 3:i + 6]
for date in validate_dates:
df = train[train.first_day_of_month == date].copy()
train.loc[train.first_day_of_month == date, 'prediction'] = df.cfips.apply(lambda cfips: last_value_dict[cfips])
smapes = train[train.first_day_of_month.isin(validate_dates)].groupby('first_day_of_month').apply(cauclate_smape)
COLS = ['GEO_ID', 'NAME', 'S0101_C01_026E']
df2020 = pd.read_csv('/kaggle/input/census-data-for-godaddy/ACSST5Y2020.S0101-Data.csv', usecols=COLS)
df2020 = df2020.iloc[1:]
df2020['S0101_C01_026E'] = df2020['S0101_C01_026E'].astype('int')
df2021 = pd.read_csv('/kaggle/input/census-data-for-godaddy/ACSST5Y2021.S0101-Data.csv', usecols=COLS)
df2021 = df2021.iloc[1:]
df2021['S0101_C01_026E'] = df2021['S0101_C01_026E'].astype('int')
df2020['cfips'] = df2020.GEO_ID.apply(lambda x: int(x.split('US')[-1]))
adult2020 = df2020.set_index('cfips').S0101_C01_026E.to_dict()
df2021['cfips'] = df2021.GEO_ID.apply(lambda x: int(x.split('US')[-1]))
adult2021 = df2021.set_index('cfips').S0101_C01_026E.to_dict()
zero_cfips = list(train[train.microbusiness_density == 0].cfips.unique())
for cfips in zero_cfips:
df = train[train.cfips == cfips].copy()
df['microbusiness_density_imputation'] = df.iloc[-1][CFG.target_field]
train.loc[train.cfips == cfips, CFG.target_field] = df['microbusiness_density_imputation']
train['active'].replace(0, 1, inplace=True)
for cfips in tqdm(test.cfips.unique()):
test.loc[test.cfips == cfips, CFG.target_field] = train[train.cfips == cfips].iloc[-1][CFG.target_field]
test['adult2020'] = test.cfips.map(adult2020)
test['adult2021'] = test.cfips.map(adult2021)
test.microbusiness_density = test.microbusiness_density * test.adult2020 / test.adult2021
test[['row_id', CFG.target_field]].to_csv('submission.csv', index=False) | code |
122258520/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/train.csv')
test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/test.csv')
revealed_test = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/revealed_test.csv')
census = pd.read_csv('/kaggle/input/godaddy-microbusiness-density-forecasting/census_starter.csv')
revealed_test_dates = revealed_test.first_day_of_month.unique()
train = pd.concat([train, revealed_test])
train.index = np.arange(0, len(train))
dates = train.first_day_of_month.unique()
dates | code |
105190901/cell_13 | [
"text_plain_output_1.png"
] | data = get_data(80) | code |
105190901/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # to show result
df = pd.DataFrame(data=data)
df.head(1) | code |
105190901/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # to show result
df = pd.DataFrame(data=data)
print(f'Number of rows is {df.shape[0]}')
print(f'Number of Nones is {df.isna().sum().sum()} in a column {df.columns[df.isna().any()].tolist()[0]}') | code |
18105662/cell_13 | [
"text_plain_output_1.png"
] | from concurrent.futures import ProcessPoolExecutor as PoolExecutor, as_completed
from google.cloud import automl_v1beta1
from tqdm import tqdm
import operator
import os
import pandas as pd
model_id = 'ICN8032497920993558639'
score_threshold = 1e-06
gcp_service_account_json = '/kaggle/input/gcloudserviceaccountkey/kaggle-playground-170215-4ece6a076f22.json'
gcp_project_id = 'kaggle-playground-170215'
def get_prediction(file_path, project_id, model_id):
name = 'projects/{}/locations/us-central1/models/{}'.format(project_id, model_id)
with open(file_path, 'rb') as ff:
content = ff.read()
payload = {'image': {'image_bytes': content}}
params = {'score_threshold': str(score_threshold)}
request = prediction_client.predict(name, payload, params)
return request
def make_int(s):
try:
int(s)
return int(s)
except ValueError:
return 1109
def process(i, df_sample_submission, project_id):
id_code = df_sample_submission.index[i]
if id_code in df_solution.index:
return None
exp_len = id_code.find('_')
experiment = id_code[0:exp_len]
plate = id_code[exp_len + 1:exp_len + 2]
well = id_code[exp_len + 3:]
pred_dict = {}
res = []
for site in range(1, 3):
file_path = '../input/recursion_rgb_512/testrgb512/testRGB512/{}_{}_{}_s{}.png'.format(experiment, plate, well, site)
try:
prediction_request = get_prediction(file_path, project_id, model_id)
except Exception as e:
return None
for prediction in prediction_request.payload:
label = make_int(prediction.display_name)
if label <= 1108:
pred_dict[label] = float(prediction.classification.score)
sirna_prediction = max(pred_dict.items(), key=operator.itemgetter(1))[0]
confidence = pred_dict[sirna_prediction]
res.append({'id_code': id_code, 'site': site, 'sirna_prediction': sirna_prediction, 'confidence': confidence})
return res
def generated_predictions_with_pool_executor(max_workers, gcp_project_id):
results = []
df_sample_submission = pd.read_csv('../input/recursion-cellular-image-classification/sample_submission.csv', index_col=[0])
with PoolExecutor(max_workers=max_workers) as executor:
futures_list = [executor.submit(process, i, df_sample_submission, gcp_project_id) for i in range(len(df_sample_submission))]
for f in tqdm(as_completed(futures_list), total=len(futures_list)):
results.append(f.result())
nb_escaped = 0
for r in results:
if r is None:
nb_escaped += 1
continue
for site in r:
df_solution.loc[site['id_code'], ['site{}_sirna'.format(site['site']), 'site{}_confidence'.format(site['site'])]] = [site['sirna_prediction'], site['confidence']]
df_solution.to_csv('./submissions/submission_{}.csv'.format(model_id))
solution_file_path = ',/submissions/submission_{}.csv'.format(model_id)
if os.path.exists(solution_file_path):
df_solution = pd.read_csv(solution_file_path, index_col=[0])
else:
df_solution = pd.DataFrame(columns=['site1_sirna', 'site1_confidence', 'site2_sirna', 'site2_confidence'])
df_solution.index.name = 'id_code'
prediction_client = automl_v1beta1.PredictionServiceClient.from_service_account_json(gcp_service_account_json)
generated_predictions_with_pool_executor(20, gcp_project_id)
generated_predictions_with_pool_executor(5, gcp_project_id)
generated_predictions_with_pool_executor(5, gcp_project_id) | code |
18105662/cell_4 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | #AutoML package
!pip install google-cloud-automl | code |
34134222/cell_2 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34134222/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
master_df = pd.read_csv('/kaggle/input/tft-match-data/TFT_Master_MatchData.csv')
time_last = master_df[['gameId', 'gameDuration']].drop_duplicates().gameDuration.agg(['min', 'mean', 'max']).to_frame()
time_last.gameDuration = time_last.gameDuration.apply(lambda x: round(x / 60))
time_last.rename(columns={'gameDuration': 'gameDuration (min)'}, inplace=True)
time_last | code |
34134222/cell_8 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
master_df = pd.read_csv('/kaggle/input/tft-match-data/TFT_Master_MatchData.csv')
time_last = master_df[['gameId', 'gameDuration']].drop_duplicates().gameDuration.agg(['min', 'mean', 'max']).to_frame()
time_last.gameDuration = time_last.gameDuration.apply(lambda x: round(x / 60))
time_last.rename(columns={'gameDuration': 'gameDuration (min)'}, inplace=True)
time_last
px.bar(time_last, x=time_last.index, y=time_last.values) | code |
34134222/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
master_df = pd.read_csv('/kaggle/input/tft-match-data/TFT_Master_MatchData.csv')
master_df.head(8) | code |
74070921/cell_4 | [
"text_plain_output_1.png"
] | from configparser import ConfigParser
from configparser import ConfigParser
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import os
import os
import tensorflow as tf
import tensorflow as tf
import numpy as np
import pandas as pd
import os
# -*- coding: utf-8 -*-
# @Author: Yulin Liu
# @Date: 2018-10-10 14:23:23
# @Last Modified by: Yulin Liu
# @Last Modified time: 2018-10-10 22:20:47
import numpy as np
import tensorflow as tf
import os
from configparser import ConfigParser
# from rnn_encoder_decoder import LSTM_model
import matplotlib.pyplot as plt
class visual_graph:
def __init__(self,
conf_path,
restored_model_path):
self.restored_model_path = restored_model_path
self.conf_path = conf_path
self.load_configs()
def load_configs(self):
parser = ConfigParser(os.environ)
parser.read(self.conf_path)
config_header = 'nn'
self.n_input = parser.getint(config_header, 'n_input')
self.n_channels = parser.getint('convolution', 'n_channels')
self.n_controled_var = parser.getint('lstm', 'n_controled_var')
self.n_encode = parser.getint(config_header, 'n_encode')
self.state_size = parser.getint('lstm', 'n_cell_dim')
self.n_layer = parser.getint('lstm', 'n_lstm_layers')
# Number of contextual samples to include
self.batch_size = parser.getint(config_header, 'batch_size')
def define_placeholder(self):
# define placeholder
self.input_encode_tensor = tf.placeholder(dtype = tf.float32, shape = [None, None, self.n_encode], name = 'encode_tensor')
self.seq_len_encode = tf.placeholder(dtype = tf.int32, shape = [None], name = 'seq_length_encode')
self.input_tensor = tf.placeholder(dtype = tf.float32, shape = [None, None, self.n_input, self.n_input, self.n_channels], name = 'decode_feature_map')
self.input_decode_coords_tensor = tf.placeholder(dtype = tf.float32, shape = [None, None, self.n_controled_var], name = 'decode_coords')
self.target = tf.placeholder(dtype = tf.float32, shape = [None, None, self.n_controled_var], name = 'target')
self.target_end = tf.placeholder(dtype = tf.float32, shape = [None, None, 1], name = 'target_end')
self.target_end_neg = tf.placeholder(dtype = tf.float32, shape = [None, None, 1], name = 'target_end_neg')
self.seq_length = tf.placeholder(dtype = tf.int32, shape = [None], name = 'seq_length_decode')
return
def launchGraph(self):
self.define_placeholder()
self.MODEL = LSTM_model(conf_path = self.conf_path,
batch_x = self.input_encode_tensor,
seq_length = self.seq_len_encode,
n_input = self.n_encode,
batch_x_decode = self.input_tensor,
batch_xcoords_decode = self.input_decode_coords_tensor,
seq_length_decode = self.seq_length,
n_input_decode = self.n_input,
target = self.target,
train = False,
weight_summary = False)
return
def feed_fwd_convlayer(self, feed_input):
with tf.device('/cpu:0'):
self.graph = tf.Graph()
self.launchGraph()
self.sess = tf.Session()
self.saver = tf.train.Saver()
self.saver.restore(self.sess, self.restored_model_path)
self.sess.graph.finalize()
self.weights = self._return_weights()
conv1_out, conv2_out, conv3_out = self._feed_fwd_convlayer(feed_input)
self.sess.close()
return conv1_out, conv2_out, conv3_out
def _return_weights(self):
weight_list = tf.trainable_variables()
weights = {}
for v in weight_list:
weights[v.name] = self.sess.run(v)
return weights
def _feed_fwd_convlayer(self, feed_input):
# feed_input should have the shape of [?, ?, 20, 20, 4]
conv1_out = self.sess.run(self.MODEL.conv1, feed_dict={self.input_tensor: feed_input})
conv2_out = self.sess.run(self.MODEL.conv2, feed_dict={self.input_tensor: feed_input})
conv3_out = self.sess.run(self.MODEL.conv3, feed_dict={self.input_tensor: feed_input})
return conv1_out, conv2_out, conv3_out
def visualize_raw_weights(weight_var, fig_size = (8, 4)):
n_layers = weight_var.shape[3]
n_channels = weight_var.shape[2]
fig, axs = plt.subplots(n_channels, n_layers, figsize=fig_size, facecolor='w', edgecolor='k')
axs = axs.ravel()
for i in range(n_channels):
for j in range(n_layers):
axs[n_layers * i + j].imshow(weight_var[:, :, i, j],
cmap = 'bwr',
vmax = weight_var.max(),
vmin = weight_var.min())
axs[n_layers * i + j].set_axis_off()
plt.show()
return fig
def visualize_conv_layers(conv_layer,
nrow,
ncol,
fig_size):
print(conv_layer.shape)
# n_layers = weight_var.shape[3]
# n_channels = weight_var.shape[2]
fig, axs = plt.subplots(nrow, ncol, figsize=fig_size, facecolor='w', edgecolor='k')
fig.subplots_adjust(wspace = 0.01, hspace = 0.01)
axs = axs.ravel()
for i in range(nrow):
for j in range(ncol):
axs[ncol * i + j].imshow(conv_layer[j, :, :, i],
cmap = 'bwr',
vmax = conv_layer[:, :, :, i].max(),
vmin = conv_layer[:, :, :, i].min(),
origin = 'lower')
axs[ncol * i + j].set_axis_off()
plt.show()
return fig
'''
Example Code:
'''
'''
tf.reset_default_graph()
restored_model_path = 'visual_network/model.ckpt-99'
config_path = 'configs/encoder_decoder_nn.ini'
visual_graph_class = visual_graph(config_path, restored_model_path)
visual_graph_class.restore_model()
weights = visual_graph_class.weights
visualize_raw_weights(weight_var=weights['wc1:0'], fig_size = (8, 2))
visualize_raw_weights(weight_var=weights['wc2:0'], fig_size = (8,4))
visualize_raw_weights(weight_var=weights['wc3:0'], fig_size = (8,4))
'''
import numpy as np
import tensorflow as tf
import os
from configparser import ConfigParser
import matplotlib.pyplot as plt
class visual_graph:
def __init__(self, conf_path, restored_model_path):
self.restored_model_path = restored_model_path
self.conf_path = conf_path
self.load_configs()
def load_configs(self):
parser = ConfigParser(os.environ)
parser.read(self.conf_path)
config_header = 'nn'
self.n_input = parser.getint(config_header, 'n_input')
self.n_channels = parser.getint('convolution', 'n_channels')
self.n_controled_var = parser.getint('input_dimension', 'n_controled_var')
self.n_coords_var = parser.getint('input_dimension', 'n_coords_var')
self.n_encode = parser.getint(config_header, 'n_encode')
self.state_size = parser.getint('lstm', 'n_cell_dim')
self.n_layer = parser.getint('lstm', 'n_lstm_layers')
self.batch_size = parser.getint(config_header, 'batch_size')
def define_placeholder(self):
self.input_encode_tensor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_encode], name='encode_tensor')
self.seq_len_encode = tf.placeholder(dtype=tf.int32, shape=[None], name='seq_length_encode')
self.input_tensor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_input, self.n_input, self.n_channels], name='decode_feature_map')
self.input_decode_coords_tensor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_controled_var + self.n_coords_var + 1], name='decode_coords')
self.target = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_controled_var + self.n_coords_var], name='target')
self.seq_length = tf.placeholder(dtype=tf.int32, shape=[None], name='seq_length_decode')
return
def launchGraph(self):
self.define_placeholder()
self.MODEL = LSTM_model(conf_path=self.conf_path, batch_x=self.input_encode_tensor, seq_length=self.seq_len_encode, n_input=self.n_encode, batch_x_decode=self.input_tensor, batch_xcoords_decode=self.input_decode_coords_tensor, seq_length_decode=self.seq_length, n_input_decode=self.n_input, target=self.target, train=False, weight_summary=False)
return
def feed_fwd_convlayer(self, feed_input):
with tf.device('/cpu:0'):
self.graph = tf.Graph()
self.launchGraph()
self.sess = tf.Session()
self.saver = tf.train.Saver()
self.saver.restore(self.sess, self.restored_model_path)
self.sess.graph.finalize()
self.weights = self._return_weights()
conv1_out, conv2_out, conv3_out, dense_out = self._feed_fwd_convlayer(feed_input)
self.sess.close()
return (conv1_out, conv2_out, conv3_out, dense_out)
def _return_weights(self):
weight_list = tf.trainable_variables()
weights = {}
for v in weight_list:
weights[v.name] = self.sess.run(v)
return weights
def _feed_fwd_convlayer(self, feed_input):
conv1_out = self.sess.run(self.MODEL.conv1, feed_dict={self.input_tensor: feed_input})
conv2_out = self.sess.run(self.MODEL.conv2, feed_dict={self.input_tensor: feed_input})
conv3_out = self.sess.run(self.MODEL.conv3, feed_dict={self.input_tensor: feed_input})
dense_out = self.sess.run(self.MODEL.fc1, feed_dict={self.input_tensor: feed_input})
return (conv1_out, conv2_out, conv3_out, dense_out)
def visualize_raw_weights(weight_var, fig_size=(8, 4)):
n_layers = weight_var.shape[3]
n_channels = weight_var.shape[2]
fig, axs = plt.subplots(n_channels, n_layers, figsize=fig_size, facecolor='w', edgecolor='k')
axs = axs.ravel()
for i in range(n_channels):
for j in range(n_layers):
axs[n_layers * i + j].imshow(weight_var[:, :, i, j], cmap='bwr', vmax=weight_var.max(), vmin=weight_var.min())
axs[n_layers * i + j].set_axis_off()
plt.show()
return fig
def visualize_conv_layers(conv_layer, nrow, ncol, fig_size):
print(conv_layer.shape)
fig, axs = plt.subplots(nrow, ncol, figsize=fig_size, facecolor='w', edgecolor='k')
fig.subplots_adjust(wspace=0.01, hspace=0.01)
axs = axs.ravel()
for i in range(nrow):
for j in range(ncol):
axs[ncol * i + j].imshow(conv_layer[j, :, :, i], cmap='bwr', vmax=conv_layer[:, :, :, i].max(), vmin=conv_layer[:, :, :, i].min(), origin='lower')
axs[ncol * i + j].set_axis_off()
plt.show()
return fig
'\nExample Code:\n'
"\ntf.reset_default_graph()\nrestored_model_path = 'visual_network/model.ckpt-99'\nconfig_path = 'configs/encoder_decoder_nn.ini'\nvisual_graph_class = visual_graph(config_path, restored_model_path)\nvisual_graph_class.restore_model()\nweights = visual_graph_class.weights\nvisualize_raw_weights(weight_var=weights['wc1:0'], fig_size = (8, 2))\nvisualize_raw_weights(weight_var=weights['wc2:0'], fig_size = (8,4))\nvisualize_raw_weights(weight_var=weights['wc3:0'], fig_size = (8,4))\n" | code |
74070921/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 |
74070921/cell_3 | [
"text_plain_output_1.png"
] | from configparser import ConfigParser
import matplotlib.pyplot as plt
import os
import os
import tensorflow as tf
import numpy as np
import pandas as pd
import os
import numpy as np
import tensorflow as tf
import os
from configparser import ConfigParser
import matplotlib.pyplot as plt
class visual_graph:
def __init__(self, conf_path, restored_model_path):
self.restored_model_path = restored_model_path
self.conf_path = conf_path
self.load_configs()
def load_configs(self):
parser = ConfigParser(os.environ)
parser.read(self.conf_path)
config_header = 'nn'
self.n_input = parser.getint(config_header, 'n_input')
self.n_channels = parser.getint('convolution', 'n_channels')
self.n_controled_var = parser.getint('lstm', 'n_controled_var')
self.n_encode = parser.getint(config_header, 'n_encode')
self.state_size = parser.getint('lstm', 'n_cell_dim')
self.n_layer = parser.getint('lstm', 'n_lstm_layers')
self.batch_size = parser.getint(config_header, 'batch_size')
def define_placeholder(self):
self.input_encode_tensor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_encode], name='encode_tensor')
self.seq_len_encode = tf.placeholder(dtype=tf.int32, shape=[None], name='seq_length_encode')
self.input_tensor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_input, self.n_input, self.n_channels], name='decode_feature_map')
self.input_decode_coords_tensor = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_controled_var], name='decode_coords')
self.target = tf.placeholder(dtype=tf.float32, shape=[None, None, self.n_controled_var], name='target')
self.target_end = tf.placeholder(dtype=tf.float32, shape=[None, None, 1], name='target_end')
self.target_end_neg = tf.placeholder(dtype=tf.float32, shape=[None, None, 1], name='target_end_neg')
self.seq_length = tf.placeholder(dtype=tf.int32, shape=[None], name='seq_length_decode')
return
def launchGraph(self):
self.define_placeholder()
self.MODEL = LSTM_model(conf_path=self.conf_path, batch_x=self.input_encode_tensor, seq_length=self.seq_len_encode, n_input=self.n_encode, batch_x_decode=self.input_tensor, batch_xcoords_decode=self.input_decode_coords_tensor, seq_length_decode=self.seq_length, n_input_decode=self.n_input, target=self.target, train=False, weight_summary=False)
return
def feed_fwd_convlayer(self, feed_input):
with tf.device('/cpu:0'):
self.graph = tf.Graph()
self.launchGraph()
self.sess = tf.Session()
self.saver = tf.train.Saver()
self.saver.restore(self.sess, self.restored_model_path)
self.sess.graph.finalize()
self.weights = self._return_weights()
conv1_out, conv2_out, conv3_out = self._feed_fwd_convlayer(feed_input)
self.sess.close()
return (conv1_out, conv2_out, conv3_out)
def _return_weights(self):
weight_list = tf.trainable_variables()
weights = {}
for v in weight_list:
weights[v.name] = self.sess.run(v)
return weights
def _feed_fwd_convlayer(self, feed_input):
conv1_out = self.sess.run(self.MODEL.conv1, feed_dict={self.input_tensor: feed_input})
conv2_out = self.sess.run(self.MODEL.conv2, feed_dict={self.input_tensor: feed_input})
conv3_out = self.sess.run(self.MODEL.conv3, feed_dict={self.input_tensor: feed_input})
return (conv1_out, conv2_out, conv3_out)
def visualize_raw_weights(weight_var, fig_size=(8, 4)):
n_layers = weight_var.shape[3]
n_channels = weight_var.shape[2]
fig, axs = plt.subplots(n_channels, n_layers, figsize=fig_size, facecolor='w', edgecolor='k')
axs = axs.ravel()
for i in range(n_channels):
for j in range(n_layers):
axs[n_layers * i + j].imshow(weight_var[:, :, i, j], cmap='bwr', vmax=weight_var.max(), vmin=weight_var.min())
axs[n_layers * i + j].set_axis_off()
plt.show()
return fig
def visualize_conv_layers(conv_layer, nrow, ncol, fig_size):
print(conv_layer.shape)
fig, axs = plt.subplots(nrow, ncol, figsize=fig_size, facecolor='w', edgecolor='k')
fig.subplots_adjust(wspace=0.01, hspace=0.01)
axs = axs.ravel()
for i in range(nrow):
for j in range(ncol):
axs[ncol * i + j].imshow(conv_layer[j, :, :, i], cmap='bwr', vmax=conv_layer[:, :, :, i].max(), vmin=conv_layer[:, :, :, i].min(), origin='lower')
axs[ncol * i + j].set_axis_off()
plt.show()
return fig
'\nExample Code:\n'
"\ntf.reset_default_graph()\nrestored_model_path = 'visual_network/model.ckpt-99'\nconfig_path = 'configs/encoder_decoder_nn.ini'\nvisual_graph_class = visual_graph(config_path, restored_model_path)\nvisual_graph_class.restore_model()\nweights = visual_graph_class.weights\nvisualize_raw_weights(weight_var=weights['wc1:0'], fig_size = (8, 2))\nvisualize_raw_weights(weight_var=weights['wc2:0'], fig_size = (8,4))\nvisualize_raw_weights(weight_var=weights['wc3:0'], fig_size = (8,4))\n" | code |
16147633/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes
y_train = X = train_df['label']
X_train = train_df.drop(columns='label', axis=1)
X_train.head() | code |
16147633/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
test_df.dtypes | code |
16147633/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
train_df.head() | code |
16147633/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes
test_df.dtypes
y_train = X = train_df['label']
X_train = train_df.drop(columns='label', axis=1)
y_test = X = test_df['label']
X_test = test_df.drop(columns='label', axis=1)
X_train.shape
X_train = np.array(X_train)
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = np.array(X_test)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train[0] | code |
16147633/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import tensorflow as tf
import keras
import os
print(os.listdir('../input')) | code |
16147633/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes | code |
16147633/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes
y_train = X = train_df['label']
X_train = train_df.drop(columns='label', axis=1)
X_train.shape | code |
16147633/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
test_df.head() | code |
16147633/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape)
train_df = train_df.astype('float32')
test_df = test_df.astype('float32')
train_df.dtypes
y_train = X = train_df['label']
X_train = train_df.drop(columns='label', axis=1)
y_train.head() | code |
16147633/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
plt.matshow(X_train[0]) | code |
16147633/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
model = Sequential()
model.add(Flatten(input_shape=[28 * 28])) | code |
16147633/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/fashion-mnist_train.csv')
test_df = pd.read_csv('../input/fashion-mnist_test.csv')
(train_df.shape, test_df.shape) | code |
105187784/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
house_rent_df = pd.read_csv('/content/gdrive/MyDrive/Colab Notebooks/House_Rent_Dataset.csv') | code |
50221247/cell_9 | [
"text_html_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import tensorflow as tf
model = tf.keras.models.Sequential([Input(shape=X_train.shape[1:]), Conv2D(32, 5, activation='relu', padding='same'), Conv2D(32, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Conv2D(64, 5, activation='relu', padding='same'), Conv2D(64, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Flatten(), Dense(256, activation='selu', kernel_initializer='lecun_normal'), BatchNormalization(), Dropout(0.4), Dense(10, activation='softmax', kernel_initializer='glorot_normal')])
checkpoint = tf.keras.callbacks.ModelCheckpoint('model.h5', save_best_only=True)
early_stopping = tf.keras.callbacks.EarlyStopping(patience=20)
model.compile(optimizer='nadam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=200, validation_data=(X_valid, y_valid), callbacks=[checkpoint, early_stopping])
model = tf.keras.models.load_model('model.h5')
model.evaluate(X_test, y_test) | code |
50221247/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 |
50221247/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import tensorflow as tf
model = tf.keras.models.Sequential([Input(shape=X_train.shape[1:]), Conv2D(32, 5, activation='relu', padding='same'), Conv2D(32, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Conv2D(64, 5, activation='relu', padding='same'), Conv2D(64, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Flatten(), Dense(256, activation='selu', kernel_initializer='lecun_normal'), BatchNormalization(), Dropout(0.4), Dense(10, activation='softmax', kernel_initializer='glorot_normal')])
checkpoint = tf.keras.callbacks.ModelCheckpoint('model.h5', save_best_only=True)
early_stopping = tf.keras.callbacks.EarlyStopping(patience=20)
model.compile(optimizer='nadam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=200, validation_data=(X_valid, y_valid), callbacks=[checkpoint, early_stopping]) | code |
50221247/cell_8 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
data_sub = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
model = tf.keras.models.Sequential([Input(shape=X_train.shape[1:]), Conv2D(32, 5, activation='relu', padding='same'), Conv2D(32, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Conv2D(64, 5, activation='relu', padding='same'), Conv2D(64, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Flatten(), Dense(256, activation='selu', kernel_initializer='lecun_normal'), BatchNormalization(), Dropout(0.4), Dense(10, activation='softmax', kernel_initializer='glorot_normal')])
checkpoint = tf.keras.callbacks.ModelCheckpoint('model.h5', save_best_only=True)
early_stopping = tf.keras.callbacks.EarlyStopping(patience=20)
model.compile(optimizer='nadam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=200, validation_data=(X_valid, y_valid), callbacks=[checkpoint, early_stopping])
figure = plt.figure(figsize=(15, 10))
plt.plot(pd.DataFrame(history.history))
plt.grid()
plt.ylabel('Loss / accuracy value')
plt.xlabel('Epoch')
plt.title('Loss and accuracy curves')
plt.legend(pd.DataFrame(history.history))
plt.show() | code |
50221247/cell_14 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
data_sub = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
X = data.iloc[:, 1:]
y = data.iloc[:, 0]
X = np.array(X).reshape(-1, 28, 28, 1)
model = tf.keras.models.Sequential([Input(shape=X_train.shape[1:]), Conv2D(32, 5, activation='relu', padding='same'), Conv2D(32, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Conv2D(64, 5, activation='relu', padding='same'), Conv2D(64, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Flatten(), Dense(256, activation='selu', kernel_initializer='lecun_normal'), BatchNormalization(), Dropout(0.4), Dense(10, activation='softmax', kernel_initializer='glorot_normal')])
checkpoint = tf.keras.callbacks.ModelCheckpoint('model.h5', save_best_only=True)
early_stopping = tf.keras.callbacks.EarlyStopping(patience=20)
model.compile(optimizer='nadam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=200, validation_data=(X_valid, y_valid), callbacks=[checkpoint, early_stopping])
figure = plt.figure(figsize=(15,10))
plt.plot(pd.DataFrame(history.history))
plt.grid()
plt.ylabel('Loss / accuracy value')
plt.xlabel('Epoch')
plt.title('Loss and accuracy curves')
plt.legend(pd.DataFrame(history.history))
plt.show()
model = tf.keras.models.load_model('model.h5')
model.evaluate(X_test, y_test)
data_sub = np.array(data_sub).reshape(-1, 28, 28, 1) / 255.0
preds = model.predict(data_sub)
np.argmax(preds[0])
labels = [np.argmax(x) for x in preds]
ids = [x + 1 for x in range(len(preds))]
sub = pd.DataFrame()
sub['ImageId'] = ids
sub['Label'] = labels
sub.to_csv('mnist_submission.csv', index=False)
pd.read_csv('mnist_submission.csv') | code |
50221247/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Dense, Flatten, BatchNormalization, Dropout, Conv2D, MaxPooling2D, Input, AveragePooling2D
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
data_sub = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
X = data.iloc[:, 1:]
y = data.iloc[:, 0]
X = np.array(X).reshape(-1, 28, 28, 1)
model = tf.keras.models.Sequential([Input(shape=X_train.shape[1:]), Conv2D(32, 5, activation='relu', padding='same'), Conv2D(32, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Conv2D(64, 5, activation='relu', padding='same'), Conv2D(64, 5, activation='relu', padding='same'), MaxPooling2D(pool_size=(2, 2)), BatchNormalization(), Dropout(0.3), Flatten(), Dense(256, activation='selu', kernel_initializer='lecun_normal'), BatchNormalization(), Dropout(0.4), Dense(10, activation='softmax', kernel_initializer='glorot_normal')])
checkpoint = tf.keras.callbacks.ModelCheckpoint('model.h5', save_best_only=True)
early_stopping = tf.keras.callbacks.EarlyStopping(patience=20)
model.compile(optimizer='nadam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=200, validation_data=(X_valid, y_valid), callbacks=[checkpoint, early_stopping])
model = tf.keras.models.load_model('model.h5')
model.evaluate(X_test, y_test)
data_sub = np.array(data_sub).reshape(-1, 28, 28, 1) / 255.0
preds = model.predict(data_sub)
np.argmax(preds[0]) | code |
32074095/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape
employees.iloc[0:3, 0:4]
employees[employees.Attrition == 'No']
employees_select = employees[['Attrition', 'Age', 'DistanceFromHome', 'WorkLifeBalance', 'EnvironmentSatisfaction', 'DailyRate', 'YearsAtCompany', 'YearsSinceLastPromotion']]
employees_select
employees_select.groupby('Attrition').mean()
employees_select.groupby('Attrition').mean().iloc[:, 0:4].sort_values('Attrition', ascending=False).plot.barh() | code |
32074095/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.head() | code |
32074095/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape
employees.iloc[0:3, 0:4]
employees[employees.Attrition == 'No']
employees_attrition = employees[employees.DistanceFromHome <= 10.632911]
employees_attrition.groupby('Attrition').Attrition.size()['No'] / employees.groupby('Attrition').Attrition.size()['No']
employees_attrition = employees[employees.DistanceFromHome >= 10.632911]
employees_attrition.groupby('Attrition').Attrition.size()['Yes'] / employees.groupby('Attrition').Attrition.size()['Yes'] | code |
32074095/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape | code |
32074095/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape
employees.iloc[0:3, 0:4]
employees[employees.Attrition == 'No']
employees_select = employees[['Attrition', 'Age', 'DistanceFromHome', 'WorkLifeBalance', 'EnvironmentSatisfaction', 'DailyRate', 'YearsAtCompany', 'YearsSinceLastPromotion']]
employees_select | code |
32074095/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 |
32074095/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees | code |
32074095/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape
employees.iloc[0:3, 0:4] | code |
32074095/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape
employees.iloc[0:3, 0:4]
employees[employees.Attrition == 'No'] | code |
32074095/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape
employees.iloc[0:3, 0:4]
employees[employees.Attrition == 'No']
employees_attrition = employees[employees.DistanceFromHome <= 10.632911]
employees_attrition.groupby('Attrition').Attrition.size()['No'] / employees.groupby('Attrition').Attrition.size()['No'] | code |
32074095/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
employees = pd.read_csv('/kaggle/input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
employees.set_index('Attrition')
employees
employees = employees.set_index('Attrition')
employees
employees = employees.reset_index()
employees.shape
employees.iloc[0:3, 0:4]
employees[employees.Attrition == 'No']
employees_select = employees[['Attrition', 'Age', 'DistanceFromHome', 'WorkLifeBalance', 'EnvironmentSatisfaction', 'DailyRate', 'YearsAtCompany', 'YearsSinceLastPromotion']]
employees_select
employees_select.groupby('Attrition').mean() | code |
104120345/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
data.info() | code |
104120345/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)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
data.head() | code |
104120345/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 |
104120345/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
print(len(data)) | code |
104120345/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/data-on-covid19-coronavirus/owid-covid-data.csv')
data.describe() | code |
2012241/cell_6 | [
"image_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import seaborn as sns
import math
from IPython.display import HTML
from subprocess import check_output
State_time_series = pd.read_csv('../input/State_time_series.csv', parse_dates=True)
State_time_series.Date = pd.to_datetime(State_time_series.Date)
states = set(State_time_series[~State_time_series['ZHVI_AllHomes'].isnull() & ~State_time_series['MedianSoldPrice_AllHomes'].isnull()]['RegionName'].values)
State_time_series_year = State_time_series[State_time_series['RegionName'].isin(states)].copy()
highest_cost_states = State_time_series_year[['RegionName', 'ZHVI_AllHomes']].groupby('RegionName').max().sort_values(by=['ZHVI_AllHomes'], ascending=False)[:5].index.values.tolist()
State_time_series_year = State_time_series_year[State_time_series_year.RegionName.isin(highest_cost_states)]
State_time_series_year.year = State_time_series_year.Date.dt.year
States_year_SalePrices = State_time_series_year.groupby([State_time_series_year.year, State_time_series_year.RegionName])['MedianSoldPrice_AllHomes'].mean().dropna().reset_index(name='SoldPrice')
States_year_SalePrices.pivot(index='Date', columns='RegionName', values='SoldPrice').plot(figsize=(15, 8), linewidth=3, fontsize=14)
plt.legend(fontsize=14)
plt.ylabel('MedianSoldPrice_AllHomes')
plt.xlabel('Year')
plt.title('Top 5 US states and their Median Sold Prices over the years 1996 to 2016')
plt.gca().get_xaxis().get_major_formatter().set_useOffset(False) | code |
2012241/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import seaborn as sns
import math
from IPython.display import HTML
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
90123938/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
import matplotlib.pyplot as plt # 导入绘图工具包
import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
X_train = X_train_image.reshape(60000, 28, 28, 1)
X_test = X_test_image.reshape(10000, 28, 28, 1)
y_train = to_categorical(y_train_lable, 10)
y_test = to_categorical(y_test_lable, 10)
from keras import models
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
model = models.Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, validation_split=0.3, epochs=5, batch_size=128)
score = model.evaluate(X_test, y_test)
pred = model.predict(X_test[0].reshape(1, 28, 28, 1))
print(pred[0], '转换一下格式得到：', pred.argmax())
import matplotlib.pyplot as plt
plt.imshow(X_test[0].reshape(28, 28), cmap='Greys') | code |
90123938/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
X_train = X_train_image.reshape(60000, 28, 28, 1)
X_test = X_test_image.reshape(10000, 28, 28, 1)
y_train = to_categorical(y_train_lable, 10)
y_test = to_categorical(y_test_lable, 10)
print('特征集张量形状：', X_train_image.shape)
print('第一个数据样本：\n', X_train_image[0]) | code |
90123938/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras import models
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
model = models.Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) | code |
90123938/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from keras.datasets import mnist
(X_train_image, y_train_lable), (X_test_image, y_test_lable) = mnist.load_data() | code |
90123938/cell_7 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
X_train = X_train_image.reshape(60000, 28, 28, 1)
X_test = X_test_image.reshape(10000, 28, 28, 1)
y_train = to_categorical(y_train_lable, 10)
y_test = to_categorical(y_test_lable, 10)
from keras import models
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
model = models.Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, validation_split=0.3, epochs=5, batch_size=128) | code |
90123938/cell_8 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
X_train = X_train_image.reshape(60000, 28, 28, 1)
X_test = X_test_image.reshape(10000, 28, 28, 1)
y_train = to_categorical(y_train_lable, 10)
y_test = to_categorical(y_test_lable, 10)
from keras import models
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
model = models.Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, validation_split=0.3, epochs=5, batch_size=128)
score = model.evaluate(X_test, y_test)
print('测试集预测准确率:', score[1]) | code |
90123938/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np # 导入NumPy数学工具箱
def to_categorical(y, num_classes=None, dtype='float32'):
y = np.array(y, dtype='int')
input_shape = y.shape
if input_shape and input_shape[-1] == 1 and (len(input_shape) > 1):
input_shape = tuple(input_shape[:-1])
y = y.ravel()
if not num_classes:
num_classes = np.max(y) + 1
n = y.shape[0]
categorical = np.zeros((n, num_classes), dtype=dtype)
categorical[np.arange(n), y] = 1
output_shape = input_shape + (num_classes,)
categorical = np.reshape(categorical, output_shape)
return categorical
X_train = X_train_image.reshape(60000, 28, 28, 1)
X_test = X_test_image.reshape(10000, 28, 28, 1)
y_train = to_categorical(y_train_lable, 10)
y_test = to_categorical(y_test_lable, 10)
print('数据集张量形状：', X_train.shape)
print('第一个数据标签：', y_train[0]) | code |
90123938/cell_5 | [
"text_plain_output_1.png"
] | print('第一个数据样本的标签：', y_train_lable[0]) | code |
32071949/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | def convert_news_text_to_specter_json(news_text_file):
d = {}
with open(news_text_file, 'r') as f:
print('test')
for i, l in enumerate(f):
print(l)
if i == 0:
d['paper_id'] = l
elif i == 1:
d['url'] = l
elif i == 2:
d['title'] = l
elif i == 3:
d['abstract'] = l
elif i == 4:
d['body_text'] = l
print(d)
convert_news_text_to_specter_json('/kaggle/input/news-test-articles/002.txt') | code |
2036996/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.naive_bayes import MultinomialNB
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sampleSubmission = pd.read_csv('../input/sample_submission.csv')
col = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
trainTxt = train['comment_text']
testTxt = test['comment_text']
trainTxt = trainTxt.fillna('unknown')
testTxt = testTxt.fillna('unknown')
combinedTxt = pd.concat([trainTxt, testTxt], axis=0)
vect = TfidfVectorizer(decode_error='ignore', use_idf=True, smooth_idf=True, min_df=10, ngram_range=(1, 3), lowercase=True, stop_words='english')
combinedDtm = vect.fit_transform(combinedTxt)
trainDtm = combinedDtm[:train.shape[0]]
testDtm = vect.transform(testTxt)
lrpreds = np.zeros((test.shape[0], len(col)))
nbpreds = np.zeros((test.shape[0], len(col)))
svmpreds = np.zeros((test.shape[0], len(col)))
xgbpreds = np.zeros((test.shape[0], len(col)))
loss = []
for i, j in enumerate(col):
lr = LogisticRegression(C=4)
lr.fit(trainDtm, train[j])
lrpreds[:, i] = lr.predict_proba(testDtm)[:, 1]
train_preds = lr.predict_proba(trainDtm)[:, 1]
loss.append(log_loss(train[j], train_preds))
np.mean(loss)
loss = []
for i, j in enumerate(col):
nb = MultinomialNB()
nb.fit(trainDtm, train[j])
nbpreds[:, i] = nb.predict_proba(testDtm)[:, 1]
train_preds = nb.predict_proba(trainDtm)[:, 1]
loss.append(log_loss(train[j], train_preds))
np.mean(loss)
loss = []
for i, j in enumerate(col):
xg = xgb.XGBClassifier(max_depth=7, n_estimators=200, colsample_bytree=0.8, subsample=0.8, nthread=10, learning_rate=0.1)
xg.fit(trainDtm, train[j])
xgbpreds[:, i] = xg.predict_proba(testDtm)[:, 1]
train_preds = xg.predict_proba(trainDtm)[:, 1]
loss.append(log_loss(train[j], train_preds))
np.mean(loss) | code |
2036996/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from sklearn.naive_bayes import MultinomialNB
from sklearn import svm
import xgboost as xgb
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2036996/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sampleSubmission = pd.read_csv('../input/sample_submission.csv')
col = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
trainTxt = train['comment_text']
testTxt = test['comment_text']
trainTxt = trainTxt.fillna('unknown')
testTxt = testTxt.fillna('unknown')
combinedTxt = pd.concat([trainTxt, testTxt], axis=0)
vect = TfidfVectorizer(decode_error='ignore', use_idf=True, smooth_idf=True, min_df=10, ngram_range=(1, 3), lowercase=True, stop_words='english')
combinedDtm = vect.fit_transform(combinedTxt)
trainDtm = combinedDtm[:train.shape[0]]
testDtm = vect.transform(testTxt)
lrpreds = np.zeros((test.shape[0], len(col)))
nbpreds = np.zeros((test.shape[0], len(col)))
svmpreds = np.zeros((test.shape[0], len(col)))
xgbpreds = np.zeros((test.shape[0], len(col)))
loss = []
for i, j in enumerate(col):
lr = LogisticRegression(C=4)
lr.fit(trainDtm, train[j])
lrpreds[:, i] = lr.predict_proba(testDtm)[:, 1]
train_preds = lr.predict_proba(trainDtm)[:, 1]
loss.append(log_loss(train[j], train_preds))
np.mean(loss) | code |
2036996/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss
from sklearn.naive_bayes import MultinomialNB
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sampleSubmission = pd.read_csv('../input/sample_submission.csv')
col = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
trainTxt = train['comment_text']
testTxt = test['comment_text']
trainTxt = trainTxt.fillna('unknown')
testTxt = testTxt.fillna('unknown')
combinedTxt = pd.concat([trainTxt, testTxt], axis=0)
vect = TfidfVectorizer(decode_error='ignore', use_idf=True, smooth_idf=True, min_df=10, ngram_range=(1, 3), lowercase=True, stop_words='english')
combinedDtm = vect.fit_transform(combinedTxt)
trainDtm = combinedDtm[:train.shape[0]]
testDtm = vect.transform(testTxt)
lrpreds = np.zeros((test.shape[0], len(col)))
nbpreds = np.zeros((test.shape[0], len(col)))
svmpreds = np.zeros((test.shape[0], len(col)))
xgbpreds = np.zeros((test.shape[0], len(col)))
loss = []
for i, j in enumerate(col):
lr = LogisticRegression(C=4)
lr.fit(trainDtm, train[j])
lrpreds[:, i] = lr.predict_proba(testDtm)[:, 1]
train_preds = lr.predict_proba(trainDtm)[:, 1]
loss.append(log_loss(train[j], train_preds))
np.mean(loss)
loss = []
for i, j in enumerate(col):
nb = MultinomialNB()
nb.fit(trainDtm, train[j])
nbpreds[:, i] = nb.predict_proba(testDtm)[:, 1]
train_preds = nb.predict_proba(trainDtm)[:, 1]
loss.append(log_loss(train[j], train_preds))
np.mean(loss) | code |
88079861/cell_21 | [
"image_output_1.png"
] | from sklearn.utils import shuffle
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import seaborn as sns
seed = 42
np.random.seed = seed
labels = ['NORMAL', 'PNEUMONIA']
folders = ['train', 'test', 'val']
def load_images_from_directory(main_dirictory, foldername):
total_labels = []
images = []
total_normal = 0
total_pneumonia = 0
path = os.path.join(main_dirictory, foldername)
for lab in labels:
full_path = os.path.join(path, lab)
for image in os.listdir(full_path):
img = cv2.imread(full_path + '/' + image)
img = cv2.resize(img, (150, 150))
images.append(img)
if lab == 'NORMAL':
label = 0
total_normal += 1
elif lab == 'PNEUMONIA':
label = 1
total_pneumonia += 1
total_labels.append(label)
return shuffle(images, total_labels, random_state=756349782)
def get_Label(number):
labels = {0: 'NORMAL', 1: 'PNEUMONIA'}
return labels[number]
def plot_predection(model_name):
images = []
count = 0
for i, files in enumerate(val_images):
img = cv2.resize(files, (150, 150))
img = np.expand_dims(files, axis=0)
feature = model_name.predict(img)
predection = np.argmax(feature, axis=1)
plt.xticks([])
plt.yticks([])
count += 1
if count == 30:
break
def freezing_layers(model_name):
for layer in model_name.layers:
layer.trainable = False
train_images = np.asarray(train_images, np.float32) / 255
train_labels = np.asarray(train_labels)
test_images = np.asarray(test_images, np.float32) / 255
test_labels = np.asarray(test_labels)
for i in range(30):
plt.xticks([])
plt.yticks([])
plt.figure(figsize=(15, 10))
plt.suptitle('Test Images', fontsize=20)
for i in range(30):
plt.subplot(5, 6, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.xlabel(get_Label(test_labels[i]))
plt.imshow(test_images[i], cmap=plt.cm.binary) | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.