path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
104115135/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values, cmap='Blues')
plt.show() | code |
104115135/cell_30 | [
"text_plain_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12 #number of variables for heatmap
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values,cmap="Blues")
plt.show()
df.isnull().sum()
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.loc[detect_outliers(df, df.columns[:-1])] | code |
104115135/cell_29 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12 #number of variables for heatmap
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values,cmap="Blues")
plt.show()
df.isnull().sum()
diagnostic_plots(df, 'fixed acidity', 'quality') | code |
104115135/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.head(10) | code |
104115135/cell_45 | [
"text_plain_output_1.png"
] | from collections import Counter
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
import feature_engine.transformation as vt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12 #number of variables for heatmap
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values,cmap="Blues")
plt.show()
df.isnull().sum()
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.loc[detect_outliers(df, df.columns[:-1])]
cols = ['fixed acidity', 'volatile acidity', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'sulphates', 'alcohol']
lt = vt.LogTransformer(variables=cols)
lt.fit(df)
df = lt.transform(df)
X_train, X_test, y_train, y_test = train_test_split(df.drop('quality', axis=1), df['quality'], test_size=0.3, random_state=0)
(X_train.shape, X_test.shape) | code |
104115135/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
for variable in df:
diagnostic_plots(df, variable, 'quality') | code |
104115135/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape | code |
104115135/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
plt.figure(1, figsize=(10, 10))
df['quality'].value_counts().plot.pie(autopct='%1.1f%%')
plt.show() | code |
104115135/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False) | code |
104115135/cell_3 | [
"image_output_1.png"
] | pip install feature-engine | code |
104115135/cell_35 | [
"text_html_output_1.png"
] | from collections import Counter
import feature_engine.transformation as vt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12 #number of variables for heatmap
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values,cmap="Blues")
plt.show()
df.isnull().sum()
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.loc[detect_outliers(df, df.columns[:-1])]
cols = ['fixed acidity', 'volatile acidity', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'sulphates', 'alcohol']
lt = vt.LogTransformer(variables=cols)
lt.fit(df) | code |
104115135/cell_43 | [
"text_plain_output_1.png"
] | from collections import Counter
import feature_engine.transformation as vt
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12 #number of variables for heatmap
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values,cmap="Blues")
plt.show()
df.isnull().sum()
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.loc[detect_outliers(df, df.columns[:-1])]
cols = ['fixed acidity', 'volatile acidity', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'sulphates', 'alcohol']
lt = vt.LogTransformer(variables=cols)
lt.fit(df)
df = lt.transform(df)
df.head() | code |
104115135/cell_31 | [
"image_output_1.png"
] | from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12 #number of variables for heatmap
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values,cmap="Blues")
plt.show()
df.isnull().sum()
def detect_outliers(df, features):
outlier_indices = []
for c in features:
Q1 = np.percentile(df[c], 25)
Q3 = np.percentile(df[c], 75)
IQR = Q3 - Q1
outlier_step = IQR * 1.5
outlier_list_col = df[(df[c] < Q1 - outlier_step) | (df[c] > Q3 + outlier_step)].index
outlier_indices.extend(outlier_list_col)
outlier_indices = Counter(outlier_indices)
multiple_outliers = list((i for i, v in outlier_indices.items() if v > 2))
return multiple_outliers
df.loc[detect_outliers(df, df.columns[:-1])]
diagnostic_plots(df, 'fixed acidity', 'quality') | code |
104115135/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique()
df.quality.value_counts(ascending=False)
def diagnostic_plots(df, variable, target):
pass
corr = df.corr()
plt.figure(figsize=(20, 9))
k = 12 #number of variables for heatmap
cols = corr.nlargest(k, 'quality')['quality'].index
cm = np.corrcoef(df[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values,cmap="Blues")
plt.show()
df.isnull().sum() | code |
104115135/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.quality.unique() | code |
104115135/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
df.shape
df.info() | code |
72073117/cell_4 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS | code |
72073117/cell_6 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST
GCS_DS_PATH_TEST_LS = datasets.get_gcs_path('des-test-ls')
GCS_DS_PATH_TEST_LS | code |
72073117/cell_7 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST
GCS_DS_PATH_TEST_LS = datasets.get_gcs_path('des-test-ls')
GCS_DS_PATH_TEST_LS
GCS_DS_PATH_VAL = datasets.get_gcs_path('des-val-non-ls')
GCS_DS_PATH_VAL | code |
72073117/cell_8 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST
GCS_DS_PATH_TEST_LS = datasets.get_gcs_path('des-test-ls')
GCS_DS_PATH_TEST_LS
GCS_DS_PATH_VAL = datasets.get_gcs_path('des-val-non-ls')
GCS_DS_PATH_VAL
GCS_DS_PATH_VAL_LS = datasets.get_gcs_path('des-val-ls')
GCS_DS_PATH_VAL_LS | code |
72073117/cell_3 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN | code |
72073117/cell_5 | [
"text_plain_output_1.png"
] | from kaggle_datasets import KaggleDatasets
datasets = KaggleDatasets()
GCS_DS_PATH_TRAIN = datasets.get_gcs_path('des-train-non-ls')
GCS_DS_PATH_TRAIN
GCS_DS_PATH_TRAIN_LS = datasets.get_gcs_path('des-train-ls')
GCS_DS_PATH_TRAIN_LS
GCS_DS_PATH_TEST = datasets.get_gcs_path('des-test-non-ls')
GCS_DS_PATH_TEST | code |
34122297/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df3 = df2.copy()
df3.Sex = df3.Sex.map({'male': 0, 'female': 1})
dft3 = dft2.copy()
dft3.Sex = dft3.Sex.map({'male': 0, 'female': 1})
df4 = pd.get_dummies(df3)
dft4 = pd.get_dummies(dft3)
X_train = df4.drop(columns=['PassengerId', 'Survived'])
X_test = dft4.drop(columns=['PassengerId'])
y_train = df4['Survived']
X_train.head(6) | code |
34122297/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df2.head(6) | code |
34122297/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.head(6) | code |
34122297/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
class1_survivors = DF[(DF.Survived == 1) & (DF.Pclass == 1)]
percent_class1_survivors = len(class1_survivors) / len(survivors)
class2_survivors = DF[(DF.Survived == 1) & (DF.Pclass == 2)]
percent_class2_survivors = len(class2_survivors) / len(survivors)
class3_survivors = DF[(DF.Survived == 1) & (DF.Pclass == 3)]
percent_class3_survivors = len(class3_survivors) / len(survivors)
print(percent_class1_survivors, percent_class2_survivors, percent_class3_survivors) | code |
34122297/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
train_data.head(6) | code |
34122297/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df3 = df2.copy()
df3.Sex = df3.Sex.map({'male': 0, 'female': 1})
dft3 = dft2.copy()
dft3.Sex = dft3.Sex.map({'male': 0, 'female': 1})
df4 = pd.get_dummies(df3)
dft4 = pd.get_dummies(dft3)
df4.head(6) | code |
34122297/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 |
34122297/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.isna().sum() | code |
34122297/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft.isna().sum() | code |
34122297/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df3 = df2.copy()
df3.Sex = df3.Sex.map({'male': 0, 'female': 1})
dft3 = dft2.copy()
dft3.Sex = dft3.Sex.map({'male': 0, 'female': 1})
df4 = pd.get_dummies(df3)
dft4 = pd.get_dummies(dft3)
X_train = df4.drop(columns=['PassengerId', 'Survived'])
X_test = dft4.drop(columns=['PassengerId'])
y_train = df4['Survived']
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, StratifiedKFold
sc = StandardScaler()
X_train[['Age', 'Fare']] = sc.fit_transform(X_train[['Age', 'Fare']])
X_test[['Age', 'Fare']] = sc.transform(X_test[['Age', 'Fare']])
skf = StratifiedKFold(n_splits=4, shuffle=True)
clf = LogisticRegression(max_iter=1000, C=0.1)
scores = cross_val_score(clf, X_train, y_train, cv=skf)
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
clf2 = RandomForestClassifier()
param_grid = {'criterion': ['gini', 'entropy'], 'min_impurity_decrease': np.linspace(0, 0.001, 100)}
gcv = GridSearchCV(clf2, param_grid=param_grid, cv=skf)
gcv.fit(X_train, y_train)
print(gcv.best_params_)
print(gcv.best_score_) | code |
34122297/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df3 = df2.copy()
df3.Sex = df3.Sex.map({'male': 0, 'female': 1})
dft3 = dft2.copy()
dft3.Sex = dft3.Sex.map({'male': 0, 'female': 1})
df4 = pd.get_dummies(df3)
dft4 = pd.get_dummies(dft3)
X_train = df4.drop(columns=['PassengerId', 'Survived'])
X_test = dft4.drop(columns=['PassengerId'])
y_train = df4['Survived']
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, StratifiedKFold
sc = StandardScaler()
X_train[['Age', 'Fare']] = sc.fit_transform(X_train[['Age', 'Fare']])
X_test[['Age', 'Fare']] = sc.transform(X_test[['Age', 'Fare']])
skf = StratifiedKFold(n_splits=4, shuffle=True)
clf = LogisticRegression(max_iter=1000, C=0.1)
scores = cross_val_score(clf, X_train, y_train, cv=skf)
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
clf2 = RandomForestClassifier()
param_grid = {'criterion': ['gini', 'entropy'], 'min_impurity_decrease': np.linspace(0, 0.001, 100)}
gcv = GridSearchCV(clf2, param_grid=param_grid, cv=skf)
gcv.fit(X_train, y_train)
predictions = gcv.predict(X_test)
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('my_submission.csv', index=False)
print('Your submission was successfully saved!') | code |
34122297/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
test_data.head(6) | code |
34122297/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df3 = df2.copy()
df3.Sex = df3.Sex.map({'male': 0, 'female': 1})
dft3 = dft2.copy()
dft3.Sex = dft3.Sex.map({'male': 0, 'female': 1})
df4 = pd.get_dummies(df3)
dft4 = pd.get_dummies(dft3)
X_train = df4.drop(columns=['PassengerId', 'Survived'])
X_test = dft4.drop(columns=['PassengerId'])
y_train = df4['Survived']
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, StratifiedKFold
sc = StandardScaler()
X_train[['Age', 'Fare']] = sc.fit_transform(X_train[['Age', 'Fare']])
X_test[['Age', 'Fare']] = sc.transform(X_test[['Age', 'Fare']])
skf = StratifiedKFold(n_splits=4, shuffle=True)
clf = LogisticRegression(max_iter=1000, C=0.1)
scores = cross_val_score(clf, X_train, y_train, cv=skf)
print(scores)
print(scores.mean()) | code |
34122297/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df3 = df2.copy()
df3.Sex = df3.Sex.map({'male': 0, 'female': 1})
dft3 = dft2.copy()
dft3.Sex = dft3.Sex.map({'male': 0, 'female': 1})
df3.head(6) | code |
34122297/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
df.isna().sum()
dft.isna().sum()
df2 = df.copy()
df2.Age = df.Age.fillna(df.Age.mean())
dft2 = dft.copy()
dft2.Age = dft.Age.fillna(dft.Age.mean())
dft2.Fare = dft.Fare.fillna(dft.Fare.mean())
df3 = df2.copy()
df3.Sex = df3.Sex.map({'male': 0, 'female': 1})
dft3 = dft2.copy()
dft3.Sex = dft3.Sex.map({'male': 0, 'female': 1})
df4 = pd.get_dummies(df3)
dft4 = pd.get_dummies(dft3)
dft4.head(6) | code |
34122297/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
df = train_data.drop(columns=['Name', 'Ticket', 'Cabin'])
dft = test_data.drop(columns=['Name', 'Ticket', 'Cabin'])
DF = pd.concat([df, dft])
survivors = DF[DF.Survived == 1]
women_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'female')]
percent_women_survivors = len(women_survivors) / len(survivors)
men_survivors = DF[(DF.Survived == 1) & (DF.Sex == 'male')]
percent_men_survivors = len(men_survivors) / len(survivors)
print(percent_women_survivors, percent_men_survivors) | code |
88100838/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('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = train_df['SalePrice']
data = pd.concat([train_df, test_df], ignore_index=True)
data = data.drop('SalePrice', 1)
data = data.drop('PoolQC', 1)
data = data.drop('Fence', 1)
data = data.drop('MiscFeature', 1)
data = data.drop('Alley', 1)
data.dtypes.value_counts() | code |
88100838/cell_4 | [
"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('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = train_df['SalePrice']
data = pd.concat([train_df, test_df], ignore_index=True)
data = data.drop('SalePrice', 1)
print(f'Shape of train set: {train_df.shape}')
print(f'Shape of test set: {test_df.shape}') | code |
88100838/cell_6 | [
"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('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = train_df['SalePrice']
data = pd.concat([train_df, test_df], ignore_index=True)
data = data.drop('SalePrice', 1)
train_df.columns | code |
88100838/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('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = train_df['SalePrice']
data = pd.concat([train_df, test_df], ignore_index=True)
data = data.drop('SalePrice', 1)
nans = pd.isnull(data).sum()
nans[nans > 0]
data = data.drop('PoolQC', 1)
data = data.drop('Fence', 1)
data = data.drop('MiscFeature', 1)
data = data.drop('Alley', 1)
data.dtypes.value_counts()
all_columns = data.columns.values
non_categorical = ['LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal']
categorical = [value for value in all_columns if value not in non_categorical]
data = pd.get_dummies(data)
data.fillna(0)
data[data == -np.inf] = 0
def avg(x):
s = np.sum(x, axis=0)
s = s / x.shape[0]
return s
def std(x):
m = avg(x)
s = np.subtract(x, m)
s = np.power(s, 2)
c = s.shape
s = np.sum(s, axis=0)
s = np.divide(s, c[0] - 1)
s = np.sqrt(s)
return s
def standardize(x):
A = avg(x)
S = std(x)
x = np.subtract(x, A)
x = np.divide(x, S)
return x
data_transformed = data.to_numpy()
t = standardize(data_transformed)
def cov(x):
cov = np.dot(x.T, x)
cov = np.divide(cov, x.shape[0])
return cov
tr = cov(t)
e, v = LA.eig(tr)
print(e)
print(v) | code |
88100838/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import warnings
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.model_selection import KFold
from sklearn import linear_model
from sklearn.metrics import make_scorer
from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn import svm
from sklearn.metrics import r2_score
from sklearn.ensemble import AdaBoostRegressor
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import GridSearchCV
import matplotlib.pyplot as plt
import tensorflow as tf
import seaborn
import warnings
from numpy import linalg as LA
warnings.filterwarnings('ignore')
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
88100838/cell_7 | [
"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('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = train_df['SalePrice']
data = pd.concat([train_df, test_df], ignore_index=True)
data = data.drop('SalePrice', 1)
nans = pd.isnull(data).sum()
nans[nans > 0] | code |
88100838/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('/kaggle/input/neolen-house-price-prediction/train.csv')
test_df = pd.read_csv('/kaggle/input/neolen-house-price-prediction/test.csv')
train_df = train_df.drop(['Id'], axis=1)
test_df = test_df.drop(['Id'], axis=1)
labels = train_df['SalePrice']
data = pd.concat([train_df, test_df], ignore_index=True)
data = data.drop('SalePrice', 1)
train_df.head() | code |
332331/cell_2 | [
"text_html_output_1.png"
] | 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
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
332331/cell_1 | [
"text_html_output_1.png"
] | 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
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.head() | code |
332331/cell_3 | [
"text_html_output_1.png"
] | 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
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['Age'] = train['Age'].fillna(train['Age'].median())
train.describe() | code |
130013533/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
import pickle
import tensorflow as tf
import time
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras.backend import set_session
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
from keras.layers import Reshape
from keras import layers
import datetime
from keras import initializers
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = '0'
set_session(tf.compat.v1.Session(config=config))
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.load(pickle_in)
pickle_in = open(os.path.join(path, yname), 'rb')
y = pickle.load(pickle_in)
x = resize(x, (len(x), 64, 64, 1), anti_aliasing=False)
y = resize(y, (len(y), 64, 64, 1), anti_aliasing=False)
from keras import models
from keras.models import model_from_json
model = models.load_model('/kaggle/input/weight/results/results/500_mg_04-07-20_47.h5')
model_json = model.to_json()
with open('dcgan.json', 'w') as json_file:
json_file.write(model_json)
with open('/kaggle/input/build-in-models/dcgan.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/250_wg_12-07-12_42.h5/250_wg_12-07-12_42.h5')
generator.compile(loss='mean_squared_error', optimizer=Adam(lr=2e-05))
# Making predictions and drawing them.
# First row: Occluded images
# Second row: Ground Truth images
# Third row: Predictions
import datetime
plot_path = "./"
a = 8690
b = 8700
pred=generator.predict(x[a:b])
fig = plt.figure(figsize = (20,10))
for ctr in range(10):
fig.add_subplot(3,10,ctr+1)
plt.imshow(np.reshape(x[a + ctr],(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(10 + ctr + 1))
plt.imshow(np.reshape(y[a + ctr]/255,(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(20 + ctr + 1))
plt.imshow(np.reshape(pred[ctr],(64,64)), cmap = "gray")
plt.savefig(os.path.join(plot_path,str(datetime.datetime.now().strftime('%m-%d-%H:%M'))))
import cv2
import matplotlib.image as mpimg
img = mpimg.imread('/kaggle/input/real-time-face/WithMask/5)Thivagaran_surgical1.png')
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img.shape
from skimage.transform import resize
img = resize(img, (64, 64), anti_aliasing=False)
import datetime
plot_path = './'
img = np.expand_dims(img, axis=-1)
img = np.expand_dims(img, axis=0)
import time
start_time = time.time()
pred = generator.predict(img)
inference_time = time.time() - start_time
pred = np.reshape(pred, (64, 64))
pred = cv2.cvtColor(pred, cv2.COLOR_GRAY2RGB)
plt.imshow(pred)
print(pred.shape)
print('inference time', inference_time) | code |
130013533/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from tensorflow.keras.optimizers import Adam
import tensorflow as tf
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras.backend import set_session
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
from keras.layers import Reshape
from keras import layers
import datetime
from keras import initializers
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = '0'
set_session(tf.compat.v1.Session(config=config))
from keras import models
from keras.models import model_from_json
model = models.load_model('/kaggle/input/weight/results/results/500_mg_04-07-20_47.h5')
model_json = model.to_json()
with open('dcgan.json', 'w') as json_file:
json_file.write(model_json)
with open('/kaggle/input/build-in-models/dcgan.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/250_wg_12-07-12_42.h5/250_wg_12-07-12_42.h5')
generator.compile(loss='mean_squared_error', optimizer=Adam(lr=2e-05))
print('Model compiled') | code |
130013533/cell_6 | [
"text_plain_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
import pickle
import tensorflow as tf
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras.backend import set_session
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
from keras.layers import Reshape
from keras import layers
import datetime
from keras import initializers
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = '0'
set_session(tf.compat.v1.Session(config=config))
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.load(pickle_in)
pickle_in = open(os.path.join(path, yname), 'rb')
y = pickle.load(pickle_in)
x = resize(x, (len(x), 64, 64, 1), anti_aliasing=False)
y = resize(y, (len(y), 64, 64, 1), anti_aliasing=False)
from keras import models
from keras.models import model_from_json
model = models.load_model('/kaggle/input/weight/results/results/500_mg_04-07-20_47.h5')
model_json = model.to_json()
with open('dcgan.json', 'w') as json_file:
json_file.write(model_json)
with open('/kaggle/input/build-in-models/dcgan.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/250_wg_12-07-12_42.h5/250_wg_12-07-12_42.h5')
generator.compile(loss='mean_squared_error', optimizer=Adam(lr=2e-05))
# Making predictions and drawing them.
# First row: Occluded images
# Second row: Ground Truth images
# Third row: Predictions
import datetime
plot_path = "./"
a = 8690
b = 8700
pred=generator.predict(x[a:b])
fig = plt.figure(figsize = (20,10))
for ctr in range(10):
fig.add_subplot(3,10,ctr+1)
plt.imshow(np.reshape(x[a + ctr],(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(10 + ctr + 1))
plt.imshow(np.reshape(y[a + ctr]/255,(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(20 + ctr + 1))
plt.imshow(np.reshape(pred[ctr],(64,64)), cmap = "gray")
plt.savefig(os.path.join(plot_path,str(datetime.datetime.now().strftime('%m-%d-%H:%M'))))
import cv2
import matplotlib.image as mpimg
img = mpimg.imread('/kaggle/input/real-time-face/WithMask/5)Thivagaran_surgical1.png')
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img.shape
plt.imshow(img)
from skimage.transform import resize
img = resize(img, (64, 64), anti_aliasing=False)
plt.imshow(img, cmap='gray') | code |
130013533/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.transform import resize
import os
import pickle
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.load(pickle_in)
pickle_in = open(os.path.join(path, yname), 'rb')
y = pickle.load(pickle_in)
print(x.shape)
print(y.shape)
print(type(x))
x = resize(x, (len(x), 64, 64, 1), anti_aliasing=False)
y = resize(y, (len(y), 64, 64, 1), anti_aliasing=False)
print(x.shape)
print(y.shape) | code |
130013533/cell_11 | [
"text_plain_output_1.png"
] | from PIL import Image
from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
import pickle
import tensorflow as tf
import tensorflow as tf
import time
import time
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras.backend import set_session
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
from keras.layers import Reshape
from keras import layers
import datetime
from keras import initializers
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = '0'
set_session(tf.compat.v1.Session(config=config))
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.load(pickle_in)
pickle_in = open(os.path.join(path, yname), 'rb')
y = pickle.load(pickle_in)
x = resize(x, (len(x), 64, 64, 1), anti_aliasing=False)
y = resize(y, (len(y), 64, 64, 1), anti_aliasing=False)
from keras import models
from keras.models import model_from_json
model = models.load_model('/kaggle/input/weight/results/results/500_mg_04-07-20_47.h5')
model_json = model.to_json()
with open('dcgan.json', 'w') as json_file:
json_file.write(model_json)
with open('/kaggle/input/build-in-models/dcgan.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/250_wg_12-07-12_42.h5/250_wg_12-07-12_42.h5')
generator.compile(loss='mean_squared_error', optimizer=Adam(lr=2e-05))
# Making predictions and drawing them.
# First row: Occluded images
# Second row: Ground Truth images
# Third row: Predictions
import datetime
plot_path = "./"
a = 8690
b = 8700
pred=generator.predict(x[a:b])
fig = plt.figure(figsize = (20,10))
for ctr in range(10):
fig.add_subplot(3,10,ctr+1)
plt.imshow(np.reshape(x[a + ctr],(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(10 + ctr + 1))
plt.imshow(np.reshape(y[a + ctr]/255,(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(20 + ctr + 1))
plt.imshow(np.reshape(pred[ctr],(64,64)), cmap = "gray")
plt.savefig(os.path.join(plot_path,str(datetime.datetime.now().strftime('%m-%d-%H:%M'))))
import cv2
import matplotlib.image as mpimg
img = mpimg.imread('/kaggle/input/real-time-face/WithMask/5)Thivagaran_surgical1.png')
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img.shape
from skimage.transform import resize
img = resize(img, (64, 64), anti_aliasing=False)
import datetime
plot_path = './'
img = np.expand_dims(img, axis=-1)
img = np.expand_dims(img, axis=0)
import time
start_time = time.time()
pred = generator.predict(img)
inference_time = time.time() - start_time
pred = np.reshape(pred, (64, 64))
pred = cv2.cvtColor(pred, cv2.COLOR_GRAY2RGB)
from PIL import Image
import tensorflow as tf
import time
print('in_predict')
with open('/kaggle/input/build-in-models/model.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/model.h5')
generator.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print('Model compiled')
low_resolution_image = cv2.imread('/kaggle/input/objects-image/compress_image_32/compress_image_32/image00000.png')
if low_resolution_image.shape >= (32, 32, 3):
low_resolution_image = cv2.resize(low_resolution_image, (32, 32))
else:
print('error')
low_resolution_image = cv2.cvtColor(low_resolution_image, cv2.COLOR_BGR2RGB)
low_resolution_image = low_resolution_image / 255.0
low_resolution_image = np.expand_dims(low_resolution_image, axis=0)
start_time = time.time()
high_resolution_image = generator.predict(low_resolution_image)
inference_time = time.time() - start_time
high_resolution_image = np.squeeze(high_resolution_image, axis=0)
high_resolution_image = cv2.normalize(high_resolution_image, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
img = Image.fromarray(high_resolution_image)
plt.imshow(high_resolution_image)
img.save('high_resolution_image.png')
print('Inference time: {} seconds'.format(inference_time)) | code |
130013533/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
import pickle
import tensorflow as tf
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras.backend import set_session
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
from keras.layers import Reshape
from keras import layers
import datetime
from keras import initializers
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = '0'
set_session(tf.compat.v1.Session(config=config))
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.load(pickle_in)
pickle_in = open(os.path.join(path, yname), 'rb')
y = pickle.load(pickle_in)
x = resize(x, (len(x), 64, 64, 1), anti_aliasing=False)
y = resize(y, (len(y), 64, 64, 1), anti_aliasing=False)
from keras import models
from keras.models import model_from_json
model = models.load_model('/kaggle/input/weight/results/results/500_mg_04-07-20_47.h5')
model_json = model.to_json()
with open('dcgan.json', 'w') as json_file:
json_file.write(model_json)
with open('/kaggle/input/build-in-models/dcgan.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/250_wg_12-07-12_42.h5/250_wg_12-07-12_42.h5')
generator.compile(loss='mean_squared_error', optimizer=Adam(lr=2e-05))
# Making predictions and drawing them.
# First row: Occluded images
# Second row: Ground Truth images
# Third row: Predictions
import datetime
plot_path = "./"
a = 8690
b = 8700
pred=generator.predict(x[a:b])
fig = plt.figure(figsize = (20,10))
for ctr in range(10):
fig.add_subplot(3,10,ctr+1)
plt.imshow(np.reshape(x[a + ctr],(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(10 + ctr + 1))
plt.imshow(np.reshape(y[a + ctr]/255,(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(20 + ctr + 1))
plt.imshow(np.reshape(pred[ctr],(64,64)), cmap = "gray")
plt.savefig(os.path.join(plot_path,str(datetime.datetime.now().strftime('%m-%d-%H:%M'))))
import cv2
import matplotlib.image as mpimg
img = mpimg.imread('/kaggle/input/real-time-face/WithMask/5)Thivagaran_surgical1.png')
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img.shape
from skimage.transform import resize
img = resize(img, (64, 64), anti_aliasing=False)
import datetime
plot_path = './'
img = np.expand_dims(img, axis=-1)
print(img.shape) | code |
130013533/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import cv2
import datetime
import datetime
import datetime
import matplotlib.image as mpimg
import numpy as np
import os
import pickle
import tensorflow as tf
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras.backend import set_session
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
from keras.layers import Reshape
from keras import layers
import datetime
from keras import initializers
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = '0'
set_session(tf.compat.v1.Session(config=config))
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.load(pickle_in)
pickle_in = open(os.path.join(path, yname), 'rb')
y = pickle.load(pickle_in)
x = resize(x, (len(x), 64, 64, 1), anti_aliasing=False)
y = resize(y, (len(y), 64, 64, 1), anti_aliasing=False)
from keras import models
from keras.models import model_from_json
model = models.load_model('/kaggle/input/weight/results/results/500_mg_04-07-20_47.h5')
model_json = model.to_json()
with open('dcgan.json', 'w') as json_file:
json_file.write(model_json)
with open('/kaggle/input/build-in-models/dcgan.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/250_wg_12-07-12_42.h5/250_wg_12-07-12_42.h5')
generator.compile(loss='mean_squared_error', optimizer=Adam(lr=2e-05))
# Making predictions and drawing them.
# First row: Occluded images
# Second row: Ground Truth images
# Third row: Predictions
import datetime
plot_path = "./"
a = 8690
b = 8700
pred=generator.predict(x[a:b])
fig = plt.figure(figsize = (20,10))
for ctr in range(10):
fig.add_subplot(3,10,ctr+1)
plt.imshow(np.reshape(x[a + ctr],(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(10 + ctr + 1))
plt.imshow(np.reshape(y[a + ctr]/255,(64,64)), cmap = "gray")
for ctr in range(10):
fig.add_subplot(3,10,(20 + ctr + 1))
plt.imshow(np.reshape(pred[ctr],(64,64)), cmap = "gray")
plt.savefig(os.path.join(plot_path,str(datetime.datetime.now().strftime('%m-%d-%H:%M'))))
import cv2
import matplotlib.image as mpimg
img = mpimg.imread('/kaggle/input/real-time-face/WithMask/5)Thivagaran_surgical1.png')
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img.shape
from skimage.transform import resize
img = resize(img, (64, 64), anti_aliasing=False)
import datetime
plot_path = './'
img = np.expand_dims(img, axis=-1)
img = np.expand_dims(img, axis=0)
print(img.shape) | code |
130013533/cell_5 | [
"text_plain_output_1.png"
] | from keras import models
from keras.backend import set_session
from skimage.transform import resize
from tensorflow.keras.optimizers import Adam
import datetime
import datetime
import numpy as np
import os
import pickle
import tensorflow as tf
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import os
import csv
import keras
import tensorflow as tf
from keras import backend
from keras.backend import set_session
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Activation, Flatten, Dense, Dropout, BatchNormalization
from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Dropout, LeakyReLU, Conv2DTranspose, ReLU
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
from keras.layers import Reshape
from keras import layers
import datetime
from keras import initializers
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.visible_device_list = '0'
set_session(tf.compat.v1.Session(config=config))
path = '/kaggle/input/face-mask-dataset-1'
xname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_mask1.pickle'
yname = '/kaggle/input/face-mask-dataset-1/celebA_real_with_out_mask1.pickle'
pickle_in = open(os.path.join(path, xname), 'rb')
x = pickle.load(pickle_in)
pickle_in = open(os.path.join(path, yname), 'rb')
y = pickle.load(pickle_in)
x = resize(x, (len(x), 64, 64, 1), anti_aliasing=False)
y = resize(y, (len(y), 64, 64, 1), anti_aliasing=False)
from keras import models
from keras.models import model_from_json
model = models.load_model('/kaggle/input/weight/results/results/500_mg_04-07-20_47.h5')
model_json = model.to_json()
with open('dcgan.json', 'w') as json_file:
json_file.write(model_json)
with open('/kaggle/input/build-in-models/dcgan.json', 'r') as json_file:
json_savedModel = json_file.read()
generator = tf.keras.models.model_from_json(json_savedModel)
generator.load_weights('/kaggle/input/build-in-models/250_wg_12-07-12_42.h5/250_wg_12-07-12_42.h5')
generator.compile(loss='mean_squared_error', optimizer=Adam(lr=2e-05))
import datetime
plot_path = './'
a = 8690
b = 8700
pred = generator.predict(x[a:b])
fig = plt.figure(figsize=(20, 10))
for ctr in range(10):
fig.add_subplot(3, 10, ctr + 1)
plt.imshow(np.reshape(x[a + ctr], (64, 64)), cmap='gray')
for ctr in range(10):
fig.add_subplot(3, 10, 10 + ctr + 1)
plt.imshow(np.reshape(y[a + ctr] / 255, (64, 64)), cmap='gray')
for ctr in range(10):
fig.add_subplot(3, 10, 20 + ctr + 1)
plt.imshow(np.reshape(pred[ctr], (64, 64)), cmap='gray')
plt.savefig(os.path.join(plot_path, str(datetime.datetime.now().strftime('%m-%d-%H:%M')))) | code |
122252967/cell_4 | [
"text_plain_output_1.png"
] | a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
print(a * 2)
print(a + a) | code |
122252967/cell_6 | [
"text_plain_output_1.png"
] | b = {'한국': '서울', '중국': '베이징', '일본': '도쿄', '미국': '워싱턴'}
for country in b:
print(f'{country}의 수도는 {b[country]} 이다') | code |
122252967/cell_2 | [
"text_plain_output_1.png"
] | x = '안녕하세요'
y = '반갑습니다'
print(type(x))
print(x + y)
print(x, y)
print(x, y, sep=',') | code |
122252967/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | c = set([1, 3, 5, 7, 9])
d = set([1, 2, 4, 6, 8])
print(c & d)
print(c | d)
print(c - d) | code |
122252967/cell_14 | [
"text_plain_output_1.png"
] | a = [1, 2, 3, 4, 5, 6, 7, 8, 9]
b = {'한국': '서울', '중국': '베이징', '일본': '도쿄', '미국': '워싱턴'}
class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def get_name(self):
return self.name
def get_age(self):
return self.age
g = Person('Dave', 27)
h = Person('Tom', 32)
print(f'{a.get_name()} is {g.get_age()} years old')
print(f'{b.get_name()} is {h.get_age()} years old') | code |
122252967/cell_10 | [
"text_plain_output_1.png"
] | e = ((0, 1), (2, 3), (4, 5))
f = (0, 1, 2, 3, 4, 5)
print(4 in e)
print(4 in f) | code |
122252967/cell_12 | [
"text_plain_output_1.png"
] | x = '안녕하세요'
y = '반갑습니다'
def number(x):
if x % 2 == 1:
return 'odd'
else:
return 'even'
num = [3, 6, 9]
[number(x) for x in num] | code |
72105169/cell_16 | [
"text_plain_output_1.png"
] | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import optuna
import pandas as pd
path = Path('/kaggle/input/house-prices-advanced-regression-techniques/')
train_ = pd.read_csv(path.joinpath('train.csv'))
test_ = pd.read_csv(path.joinpath('test.csv'))
categorical_feature = []
for col in train_.columns.tolist():
if train_[col].dtype == object:
categorical_feature.append(col)
nan_train_cols = train_.columns[1:-1][train_.iloc[:, 1:-1].isnull().sum() > 0].tolist()
for col in nan_train_cols:
if col in categorical_feature:
train_.loc[train_[col].isna(), col] = train_.loc[~train_[col].isna(), col].mode().values[0]
else:
train_.loc[train_[col].isna(), col] = train_.loc[~train_[col].isna(), col].mean()
nan_test_cols = test_.columns[1:-1][test_.iloc[:, 1:-1].isnull().sum() > 0].tolist()
for col in nan_test_cols:
if col in categorical_feature:
test_.loc[test_[col].isna(), col] = test_.loc[~test_[col].isna(), col].mode().values[0]
else:
test_.loc[test_[col].isna(), col] = test_.loc[~test_[col].isna(), col].mean()
columns_drop = ['GarageCars', 'GarageYrBlt', 'GrLivArea', 'Id', 'YearRemodAdd']
train_.drop(columns=columns_drop, inplace=True)
test_.drop(columns=columns_drop, inplace=True)
all_ = pd.concat([train_, test_])
dumy = pd.get_dummies(all_[categorical_feature])
all_ = pd.concat([all_.loc[:, ~all_.columns.isin(categorical_feature)], dumy], axis=1)
train_data = all_.iloc[0:1460, :]
test_data = all_.iloc[1460:, :]
test_data.drop(columns=['SalePrice'], inplace=True)
feature_cols = train_data.columns.tolist()
feature_cols.remove('SalePrice')
train_data['SalePrice'] = np.log(train_data['SalePrice'])
train_data, validation_data = train_test_split(train_data, test_size=0.2, random_state=42)
train_data.reset_index(drop=True, inplace=True)
validation_data.reset_index(drop=True, inplace=True)
baseline = lgbm.LGBMRegressor()
baseline.fit(train_data[feature_cols], train_data['SalePrice'])
baseline_val_y = baseline.predict(validation_data[feature_cols])
base_line_score = np.sqrt(mean_squared_error(baseline_val_y, validation_data['SalePrice'].values))
def objective(trial, x_train, y_train, x_valid, y_valid):
train_d = lgbm.Dataset(x_train, y_train)
val_d = lgbm.Dataset(x_valid, y_valid)
param = {'objective': 'regression', 'metric': 'rmse', 'verbosity': -1, 'boosting_type': trial.suggest_categorical('boosting_type', ['gbdt', 'rf', 'dart']), 'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-08, 10.0), 'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-08, 10.0), 'num_leaves': trial.suggest_int('num_leaves', 2, 10000), 'feature_fraction': trial.suggest_uniform('feature_fraction', 0.4, 1.0), 'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.4, 1.0), 'bagging_freq': trial.suggest_int('bagging_freq', 1, 7), 'min_child_samples': trial.suggest_int('min_child_samples', 5, 500)}
gbm = lgbm.train(param, train_d, valid_sets=val_d, verbose_eval=100)
off = gbm.predict(x_valid)
error = mean_squared_error(y_valid, off)
return np.sqrt(error)
x_train, x_val = (train_data[feature_cols].values, validation_data[feature_cols].values)
y_train, y_val = (train_data['SalePrice'].values, validation_data['SalePrice'].values)
study = optuna.create_study(direction='minimize')
study.optimize(lambda trial: objective(trial, x_train, y_train, x_val, y_val), n_trials=1000) | code |
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] | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import optuna
import pandas as pd
path = Path('/kaggle/input/house-prices-advanced-regression-techniques/')
train_ = pd.read_csv(path.joinpath('train.csv'))
test_ = pd.read_csv(path.joinpath('test.csv'))
categorical_feature = []
for col in train_.columns.tolist():
if train_[col].dtype == object:
categorical_feature.append(col)
nan_train_cols = train_.columns[1:-1][train_.iloc[:, 1:-1].isnull().sum() > 0].tolist()
for col in nan_train_cols:
if col in categorical_feature:
train_.loc[train_[col].isna(), col] = train_.loc[~train_[col].isna(), col].mode().values[0]
else:
train_.loc[train_[col].isna(), col] = train_.loc[~train_[col].isna(), col].mean()
nan_test_cols = test_.columns[1:-1][test_.iloc[:, 1:-1].isnull().sum() > 0].tolist()
for col in nan_test_cols:
if col in categorical_feature:
test_.loc[test_[col].isna(), col] = test_.loc[~test_[col].isna(), col].mode().values[0]
else:
test_.loc[test_[col].isna(), col] = test_.loc[~test_[col].isna(), col].mean()
columns_drop = ['GarageCars', 'GarageYrBlt', 'GrLivArea', 'Id', 'YearRemodAdd']
train_.drop(columns=columns_drop, inplace=True)
test_.drop(columns=columns_drop, inplace=True)
all_ = pd.concat([train_, test_])
dumy = pd.get_dummies(all_[categorical_feature])
all_ = pd.concat([all_.loc[:, ~all_.columns.isin(categorical_feature)], dumy], axis=1)
train_data = all_.iloc[0:1460, :]
test_data = all_.iloc[1460:, :]
test_data.drop(columns=['SalePrice'], inplace=True)
feature_cols = train_data.columns.tolist()
feature_cols.remove('SalePrice')
train_data['SalePrice'] = np.log(train_data['SalePrice'])
train_data, validation_data = train_test_split(train_data, test_size=0.2, random_state=42)
train_data.reset_index(drop=True, inplace=True)
validation_data.reset_index(drop=True, inplace=True)
baseline = lgbm.LGBMRegressor()
baseline.fit(train_data[feature_cols], train_data['SalePrice'])
baseline_val_y = baseline.predict(validation_data[feature_cols])
base_line_score = np.sqrt(mean_squared_error(baseline_val_y, validation_data['SalePrice'].values))
def objective(trial, x_train, y_train, x_valid, y_valid):
train_d = lgbm.Dataset(x_train, y_train)
val_d = lgbm.Dataset(x_valid, y_valid)
param = {'objective': 'regression', 'metric': 'rmse', 'verbosity': -1, 'boosting_type': trial.suggest_categorical('boosting_type', ['gbdt', 'rf', 'dart']), 'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-08, 10.0), 'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-08, 10.0), 'num_leaves': trial.suggest_int('num_leaves', 2, 10000), 'feature_fraction': trial.suggest_uniform('feature_fraction', 0.4, 1.0), 'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.4, 1.0), 'bagging_freq': trial.suggest_int('bagging_freq', 1, 7), 'min_child_samples': trial.suggest_int('min_child_samples', 5, 500)}
gbm = lgbm.train(param, train_d, valid_sets=val_d, verbose_eval=100)
off = gbm.predict(x_valid)
error = mean_squared_error(y_valid, off)
return np.sqrt(error)
x_train, x_val = (train_data[feature_cols].values, validation_data[feature_cols].values)
y_train, y_val = (train_data['SalePrice'].values, validation_data['SalePrice'].values)
study = optuna.create_study(direction='minimize')
study.optimize(lambda trial: objective(trial, x_train, y_train, x_val, y_val), n_trials=1000)
param = {'objective': 'regression', 'metric': 'rmse', 'verbosity': -1, 'boosting_type': 'gbdt'}
param.update(study.best_trial.params)
print(param) | code |
72105169/cell_14 | [
"text_plain_output_1.png"
] | from pathlib import Path
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split, KFold
import lightgbm as lgbm
import numpy as np
import pandas as pd
path = Path('/kaggle/input/house-prices-advanced-regression-techniques/')
train_ = pd.read_csv(path.joinpath('train.csv'))
test_ = pd.read_csv(path.joinpath('test.csv'))
categorical_feature = []
for col in train_.columns.tolist():
if train_[col].dtype == object:
categorical_feature.append(col)
nan_train_cols = train_.columns[1:-1][train_.iloc[:, 1:-1].isnull().sum() > 0].tolist()
for col in nan_train_cols:
if col in categorical_feature:
train_.loc[train_[col].isna(), col] = train_.loc[~train_[col].isna(), col].mode().values[0]
else:
train_.loc[train_[col].isna(), col] = train_.loc[~train_[col].isna(), col].mean()
nan_test_cols = test_.columns[1:-1][test_.iloc[:, 1:-1].isnull().sum() > 0].tolist()
for col in nan_test_cols:
if col in categorical_feature:
test_.loc[test_[col].isna(), col] = test_.loc[~test_[col].isna(), col].mode().values[0]
else:
test_.loc[test_[col].isna(), col] = test_.loc[~test_[col].isna(), col].mean()
columns_drop = ['GarageCars', 'GarageYrBlt', 'GrLivArea', 'Id', 'YearRemodAdd']
train_.drop(columns=columns_drop, inplace=True)
test_.drop(columns=columns_drop, inplace=True)
all_ = pd.concat([train_, test_])
dumy = pd.get_dummies(all_[categorical_feature])
all_ = pd.concat([all_.loc[:, ~all_.columns.isin(categorical_feature)], dumy], axis=1)
train_data = all_.iloc[0:1460, :]
test_data = all_.iloc[1460:, :]
test_data.drop(columns=['SalePrice'], inplace=True)
feature_cols = train_data.columns.tolist()
feature_cols.remove('SalePrice')
train_data['SalePrice'] = np.log(train_data['SalePrice'])
train_data, validation_data = train_test_split(train_data, test_size=0.2, random_state=42)
train_data.reset_index(drop=True, inplace=True)
validation_data.reset_index(drop=True, inplace=True)
baseline = lgbm.LGBMRegressor()
baseline.fit(train_data[feature_cols], train_data['SalePrice'])
baseline_val_y = baseline.predict(validation_data[feature_cols])
base_line_score = np.sqrt(mean_squared_error(baseline_val_y, validation_data['SalePrice'].values))
print(f'The base line score is : {base_line_score}') | code |
73077056/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from netmiko import ConnectHandler
import os
from netmiko import ConnectHandler
import os
os.environ['NET_TEXTFSM'] = '/opt/conda/lib/python3.7/site-packages/ntc_templates/templates'
linux = {'device_type': 'linux', 'host': '3.89.45.60', 'username': 'kevin', 'password': 'S!mpl312'}
c = ConnectHandler(**linux)
r = c.send_command('arp -a', use_textfsm=True)
print(r)
print(r[0]['ip_address'])
for item in r:
print(item)
print(item['ip_address'])
"\nEXPECTED OUTPUT:\n[{'rev_dns': '_gateway', 'ip_address': '172.30.1.1', 'mac_address': '0e:18:8d:7f:b8:65', 'hw_type': 'ether', 'interface': 'eth0'}]\n"
'\nChassis type: ASR1004 \nSlot: R0, ASR1000-RP1 \n Running state : ok, active\n Internal state : online\n Internal operational state : ok\n Physical insert detect time : 00:00:45 (2w5d ago)\n Software declared up time : 00:00:45 (2w5d ago)\n CPLD version : 07062111\n Firmware version : 12.2(33r)XNC\nSlot: F0, ASR1000-ESP10 \n Running state : ok, active\n Internal state : online\n Internal operational state : ok\n Physical insert detect time : 00:00:45 (2w5d ago)\n Software declared up time : 00:03:15 (2w5d ago)\n Hardware ready signal time : 00:00:46 (2w5d ago)\n Packet ready signal time : 00:04:00 (2w5d ago)\n CPLD version : 07091401\n Firmware version : 12.2(33r)XNC\nSlot: P0, ASR1004-PWR-AC\n State : ok\n Physical insert detect time : 00:03:08 (2w5d ago)\nSlot: P1, ASR1004-PWR-AC\n State : ok\n Physical insert d\n' | code |
73077056/cell_1 | [
"text_plain_output_1.png"
] | !pip install netmiko | code |
73077056/cell_3 | [
"text_plain_output_1.png"
] | !pip install ntc_templates | code |
34125991/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
full_scaler = MinMaxScaler()
scaled_full_data = full_scaler.fit_transform(df)
length = 12
generator_final = TimeseriesGenerator(scaled_full_data, scaled_full_data, length=length, batch_size=1)
def finalmodel():
model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modelfinal = finalmodel()
modelfinal.fit_generator(generator_final, epochs=10) | code |
34125991/cell_13 | [
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
early_stop = EarlyStopping(monitor='val_loss', patience=2)
validation_generator = TimeseriesGenerator(scaled_test, scaled_test, length=length, batch_size=1)
modeltest.fit_generator(generator, epochs=20, validation_data=validation_generator, callbacks=[early_stop]) | code |
34125991/cell_25 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
early_stop = EarlyStopping(monitor='val_loss', patience=2)
validation_generator = TimeseriesGenerator(scaled_test, scaled_test, length=length, batch_size=1)
modeltest.fit_generator(generator, epochs=20, validation_data=validation_generator, callbacks=[early_stop])
losses = pd.DataFrame(modeltest.history.history)
test_predictions = []
first_eval_batch = scaled_train[-length:]
current_batch = first_eval_batch.reshape((1, length, n_features))
for i in range(len(test)):
current_pred = modeltest.predict(current_batch)[0]
test_predictions.append(current_pred)
current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
true_predictions = scaler.inverse_transform(test_predictions)
test['Predictions'] = true_predictions
full_scaler = MinMaxScaler()
scaled_full_data = full_scaler.fit_transform(df)
length = 12
generator_final = TimeseriesGenerator(scaled_full_data, scaled_full_data, length=length, batch_size=1)
def finalmodel():
model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modelfinal = finalmodel()
modelfinal.fit_generator(generator_final, epochs=10)
forecast = []
periods = 36
first_eval_batch = scaled_full_data[-length:]
current_batch = first_eval_batch.reshape((1, length, n_features))
for i in range(periods):
current_pred = modelfinal.predict(current_batch)[0]
forecast.append(current_pred)
current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
forecast = scaler.inverse_transform(forecast)
forecast_index = pd.date_range(start='2020-04-01', periods=periods, freq='MS')
forecast_df = pd.DataFrame(data=forecast, index=forecast_index, columns=['Forecast'])
#plot the entire dataset and predictions
ax = df.plot()
forecast_df.plot(ax=ax)
ax = df.plot()
forecast_df.plot(ax=ax)
plt.xlim('2017-01-01', '2023-4-01') | code |
34125991/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
print('total entries = ', len(df))
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
display(train.head())
print('Train shape : ', train.shape)
display(test.head())
print('Train shape : ', test.shape) | code |
34125991/cell_23 | [
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
early_stop = EarlyStopping(monitor='val_loss', patience=2)
validation_generator = TimeseriesGenerator(scaled_test, scaled_test, length=length, batch_size=1)
modeltest.fit_generator(generator, epochs=20, validation_data=validation_generator, callbacks=[early_stop])
losses = pd.DataFrame(modeltest.history.history)
test_predictions = []
first_eval_batch = scaled_train[-length:]
current_batch = first_eval_batch.reshape((1, length, n_features))
for i in range(len(test)):
current_pred = modeltest.predict(current_batch)[0]
test_predictions.append(current_pred)
current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
true_predictions = scaler.inverse_transform(test_predictions)
test['Predictions'] = true_predictions
full_scaler = MinMaxScaler()
scaled_full_data = full_scaler.fit_transform(df)
length = 12
generator_final = TimeseriesGenerator(scaled_full_data, scaled_full_data, length=length, batch_size=1)
def finalmodel():
model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modelfinal = finalmodel()
modelfinal.fit_generator(generator_final, epochs=10)
forecast = []
periods = 36
first_eval_batch = scaled_full_data[-length:]
current_batch = first_eval_batch.reshape((1, length, n_features))
for i in range(periods):
current_pred = modelfinal.predict(current_batch)[0]
forecast.append(current_pred)
current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
forecast = scaler.inverse_transform(forecast)
forecast_index = pd.date_range(start='2020-04-01', periods=periods, freq='MS')
forecast_df = pd.DataFrame(data=forecast, index=forecast_index, columns=['Forecast'])
display(forecast_df.head()) | code |
34125991/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
print(f'Given the Array: \n{X.flatten()}')
print(f'Predict this y: \n {y}') | code |
34125991/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
early_stop = EarlyStopping(monitor='val_loss', patience=2)
validation_generator = TimeseriesGenerator(scaled_test, scaled_test, length=length, batch_size=1)
modeltest.fit_generator(generator, epochs=20, validation_data=validation_generator, callbacks=[early_stop])
losses = pd.DataFrame(modeltest.history.history)
test_predictions = []
first_eval_batch = scaled_train[-length:]
current_batch = first_eval_batch.reshape((1, length, n_features))
for i in range(len(test)):
current_pred = modeltest.predict(current_batch)[0]
test_predictions.append(current_pred)
current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
true_predictions = scaler.inverse_transform(test_predictions)
test['Predictions'] = true_predictions
display(test.head())
test.plot()
plt.show() | code |
34125991/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
print(df.info())
display(df.head())
df.plot(figsize=(12, 6)) | code |
34125991/cell_24 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
early_stop = EarlyStopping(monitor='val_loss', patience=2)
validation_generator = TimeseriesGenerator(scaled_test, scaled_test, length=length, batch_size=1)
modeltest.fit_generator(generator, epochs=20, validation_data=validation_generator, callbacks=[early_stop])
losses = pd.DataFrame(modeltest.history.history)
test_predictions = []
first_eval_batch = scaled_train[-length:]
current_batch = first_eval_batch.reshape((1, length, n_features))
for i in range(len(test)):
current_pred = modeltest.predict(current_batch)[0]
test_predictions.append(current_pred)
current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
true_predictions = scaler.inverse_transform(test_predictions)
test['Predictions'] = true_predictions
full_scaler = MinMaxScaler()
scaled_full_data = full_scaler.fit_transform(df)
length = 12
generator_final = TimeseriesGenerator(scaled_full_data, scaled_full_data, length=length, batch_size=1)
def finalmodel():
model = Sequential()
model.add(LSTM(100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modelfinal = finalmodel()
modelfinal.fit_generator(generator_final, epochs=10)
forecast = []
periods = 36
first_eval_batch = scaled_full_data[-length:]
current_batch = first_eval_batch.reshape((1, length, n_features))
for i in range(periods):
current_pred = modelfinal.predict(current_batch)[0]
forecast.append(current_pred)
current_batch = np.append(current_batch[:, 1:, :], [[current_pred]], axis=1)
forecast = scaler.inverse_transform(forecast)
forecast_index = pd.date_range(start='2020-04-01', periods=periods, freq='MS')
forecast_df = pd.DataFrame(data=forecast, index=forecast_index, columns=['Forecast'])
ax = df.plot()
forecast_df.plot(ax=ax) | code |
34125991/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
early_stop = EarlyStopping(monitor='val_loss', patience=2)
validation_generator = TimeseriesGenerator(scaled_test, scaled_test, length=length, batch_size=1)
modeltest.fit_generator(generator, epochs=20, validation_data=validation_generator, callbacks=[early_stop])
losses = pd.DataFrame(modeltest.history.history)
losses.plot()
plt.show() | code |
34125991/cell_10 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator
import pandas as pd
df = pd.read_csv('https://fred.stlouisfed.org/graph/fredgraph.csv?bgcolor=%23e1e9f0&chart_type=line&drp=0&fo=open%20sans&graph_bgcolor=%23ffffff&height=450&mode=fred&recession_bars=on&txtcolor=%23444444&ts=12&tts=12&width=1168&nt=0&thu=0&trc=0&show_legend=yes&show_axis_titles=yes&show_tooltip=yes&id=MRTSSM448USN&scale=left&cosd=1992-01-01&coed=2020-03-01&line_color=%234572a7&link_values=false&line_style=solid&mark_type=none&mw=3&lw=2&ost=-99999&oet=99999&mma=0&fml=a&fq=Monthly&fam=avg&fgst=lin&fgsnd=2009-06-01&line_index=1&transformation=lin&vintage_date=2020-05-16&revision_date=2020-05-16&nd=1992-01-01')
df.rename(columns={'MRTSSM448USN': 'Sales'}, inplace=True)
df['DATE'] = df['DATE'].astype('datetime64[ns]')
df.set_index('DATE', drop=True, inplace=True)
test_size = 18
test_ind = len(df) - 18
train = df.iloc[:test_ind]
test = df.iloc[test_ind:]
scaler = MinMaxScaler()
scaler.fit(train)
scaled_train = scaler.transform(train)
scaled_test = scaler.transform(test)
length = 12
generator = TimeseriesGenerator(scaled_train, scaled_train, length=length, batch_size=1)
X, y = generator[0]
n_features = 1
def testmodel():
model = Sequential()
model.add(LSTM(units=100, activation='relu', input_shape=(length, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
return model
modeltest = testmodel()
print(modeltest.summary()) | code |
18108171/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
param = {'n_neighbors': [5, 10, 15, 20, 25, 30], 'p': [2, 3, 4, 5, 6]}
gsc = GridSearchCV(knn, param, cv=5, refit=True)
gsc.fit(X_train, y_train)
gsc.best_estimator_ | code |
18108171/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
param = {'n_neighbors': [5, 10, 15, 20, 25, 30], 'p': [2, 3, 4, 5, 6]}
gsc = GridSearchCV(knn, param, cv=5, refit=True)
gsc.fit(X_train, y_train) | code |
18108171/cell_19 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
plt.figure(figsize=(6, 6))
cm = confusion_matrix(y_test, grid_predict)
sns.set(font_scale=1.25)
sns.heatmap(cm, annot=True, fmt='g', cbar=False, cmap='Blues')
plt.title('Confusion matrix') | code |
18108171/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import confusion_matrix, classification_report
print(classification_report(y_test, grid_predict)) | code |
18108171/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
param = {'n_neighbors': [5, 10, 15, 20, 25, 30], 'p': [2, 3, 4, 5, 6]}
gsc = GridSearchCV(knn, param, cv=5, refit=True)
gsc.fit(X_train, y_train)
gsc.best_estimator_
gsc.best_params_ | code |
18108171/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train) | code |
90153636/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
prediction = regr.predict([[2020, 20000]])
y_hat = regr.predict(X)
y_hat | code |
90153636/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
import sklearn
from sklearn.linear_model import LinearRegression
len(df) | code |
90153636/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
X.head() | code |
90153636/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
df.head() | code |
90153636/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
prediction = regr.predict([[2020, 20000]])
y_hat = regr.predict(X)
y_hat
regr.score(X, Y) | code |
90153636/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
Y.head() | code |
90153636/cell_8 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
print('intercept :', regr.intercept_)
print('coefficient :', regr.coef_)
print("Prediction : ['Year', 'Mileage']")
prediction = regr.predict([[2020, 20000]])
print('Price Prediction : ', prediction) | code |
90153636/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
sns.scatterplot(x=df['year'], y=df['price'], hue=df['fuelType'], data=df) | code |
90153636/cell_10 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import pandas as pd
df = pd.read_csv('../input/used-car-dataset-ford-and-mercedes/toyota.csv')
X = df[['year', 'mileage']]
Y = df['price']
regr = linear_model.LinearRegression()
regr.fit(X.values, Y)
prediction = regr.predict([[2020, 20000]])
y_hat = regr.predict(X)
y_hat
dc = pd.concat([df[0:].reset_index(), pd.Series(y_hat, name='predicted')], axis='columns')
dc | code |
49118983/cell_42 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import tensorflow.keras.layers as L
import tensorflow.keras.models as M
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
with tpu_strategy.scope():
def make_ann(n_in):
inp = L.Input(shape=(n_in,), name='inp')
d1 = L.Dense(100, activation='relu', name='d1')(inp)
d2 = L.Dense(100, activation='relu', name='d2')(d1)
preds = L.Dense(1, activation='sigmoid', name='preds')(d2)
model = M.Model(inp, preds, name='ANN')
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
return model | code |
49118983/cell_21 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
TRAIN_FILE = os.path.join(INPUT_DIR, 'train.csv')
TEST_FILE = os.path.join(INPUT_DIR, 'test.csv')
QUES_FILE = os.path.join(INPUT_DIR, 'questions.csv')
LEC_FILE = os.path.join(INPUT_DIR, 'lectures.csv')
tr = pd.read_csv(TRAIN_FILE, usecols=[1, 2, 3, 4, 7, 8, 9], dtype={'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'answered_correctly': 'int8', 'prior_question_elapsed_time': 'float32', 'prior_question_had_explanation': 'boolean'})
def ds_to_pickle(ds, ds_file, pkl_file):
ds.to_pickle(pkl_file)
del ds
return pd.read_pickle('tr.pkl')
tr = ds_to_pickle(tr, TRAIN_FILE, 'tr.pkl')
total_num_users = tr.user_id.unique().size
unique_user_ids = list(tr.user_id.unique())
total_num_ques = tr.loc[tr.content_type_id == 0].content_id.unique().size
unique_ques = list(tr.loc[tr.content_type_id == 0].content_id.unique())
num_ques_per_user = pd.DataFrame({'user_id': list(tr.loc[tr.content_type_id == 0].user_id.unique()), 'num_ques_answered': list(tr.loc[tr.content_type_id == 0].user_id.value_counts())})
num_ques_answered = num_ques_per_user.sort_values('num_ques_answered')['num_ques_answered'].to_frame(name='num_ques_answered')
print(num_ques_answered.min(), num_ques_answered.max()) | code |
49118983/cell_25 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
TRAIN_FILE = os.path.join(INPUT_DIR, 'train.csv')
TEST_FILE = os.path.join(INPUT_DIR, 'test.csv')
QUES_FILE = os.path.join(INPUT_DIR, 'questions.csv')
LEC_FILE = os.path.join(INPUT_DIR, 'lectures.csv')
tr = pd.read_csv(TRAIN_FILE, usecols=[1, 2, 3, 4, 7, 8, 9], dtype={'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'answered_correctly': 'int8', 'prior_question_elapsed_time': 'float32', 'prior_question_had_explanation': 'boolean'})
def ds_to_pickle(ds, ds_file, pkl_file):
ds.to_pickle(pkl_file)
del ds
return pd.read_pickle('tr.pkl')
tr = ds_to_pickle(tr, TRAIN_FILE, 'tr.pkl')
total_num_users = tr.user_id.unique().size
unique_user_ids = list(tr.user_id.unique())
total_num_ques = tr.loc[tr.content_type_id == 0].content_id.unique().size
unique_ques = list(tr.loc[tr.content_type_id == 0].content_id.unique())
num_ques_per_user = pd.DataFrame({'user_id': list(tr.loc[tr.content_type_id == 0].user_id.unique()), 'num_ques_answered': list(tr.loc[tr.content_type_id == 0].user_id.value_counts())})
num_ques_answered = num_ques_per_user.sort_values('num_ques_answered')['num_ques_answered'].to_frame(name='num_ques_answered')
def remove_user_by_num_ques_ans(num_ques_ans_thresh=100, tr=None):
num_ques_ans_filtered = num_ques_answered.loc[num_ques_answered.num_ques_answered > num_ques_ans_thresh].rename(columns={'num_ques_answered': 'num_ques_answered_gt_' + str(num_ques_ans_thresh)})
num_ques_per_user_gt_thresh = num_ques_per_user.loc[num_ques_per_user.num_ques_answered > num_ques_ans_thresh].rename(columns={'num_ques_answered': 'num_ques_answered_gt' + str(num_ques_ans_thresh)})
new_tr = tr[tr['user_id'].isin(list(num_ques_per_user_gt_thresh['user_id']))]
return (num_ques_per_user_gt_thresh, new_tr)
num_ques_answered_gt_100, tr_user_ques_gt_100 = remove_user_by_num_ques_ans(100, tr=tr) | code |
49118983/cell_34 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import gc
import tensorflow as tf
import tensorflow.keras.models as M
import tensorflow.keras.layers as L
import riiideducation
INPUT_DIR = '/kaggle/input/riiid-test-answer-prediction/'
TRAIN_FILE = os.path.join(INPUT_DIR, 'train.csv')
TEST_FILE = os.path.join(INPUT_DIR, 'test.csv')
QUES_FILE = os.path.join(INPUT_DIR, 'questions.csv')
LEC_FILE = os.path.join(INPUT_DIR, 'lectures.csv')
tr = pd.read_csv(TRAIN_FILE, usecols=[1, 2, 3, 4, 7, 8, 9], dtype={'timestamp': 'int64', 'user_id': 'int32', 'content_id': 'int16', 'content_type_id': 'int8', 'answered_correctly': 'int8', 'prior_question_elapsed_time': 'float32', 'prior_question_had_explanation': 'boolean'})
def ds_to_pickle(ds, ds_file, pkl_file):
ds.to_pickle(pkl_file)
del ds
return pd.read_pickle('tr.pkl')
tr = ds_to_pickle(tr, TRAIN_FILE, 'tr.pkl')
total_num_users = tr.user_id.unique().size
unique_user_ids = list(tr.user_id.unique())
total_num_ques = tr.loc[tr.content_type_id == 0].content_id.unique().size
unique_ques = list(tr.loc[tr.content_type_id == 0].content_id.unique())
num_ques_per_user = pd.DataFrame({'user_id': list(tr.loc[tr.content_type_id == 0].user_id.unique()), 'num_ques_answered': list(tr.loc[tr.content_type_id == 0].user_id.value_counts())})
num_ques_answered = num_ques_per_user.sort_values('num_ques_answered')['num_ques_answered'].to_frame(name='num_ques_answered')
new_num_rows = len(tr_user_ques_gt_100.index)
old_num_rows = len(tr.index)
tr_user_ques_gt_100.to_pickle('tr_user_ans_gt_100_ques.pkl')
tr = tr_user_ques_gt_100
TIME_MEAN = tr.prior_question_elapsed_time.median()
TIME_MIN = tr.prior_question_elapsed_time.min()
TIME_MAX = tr.prior_question_elapsed_time.max()
print(TIME_MEAN, TIME_MAX, TIME_MIN)
map_prior = {True: 1, False: 0} | code |
49118983/cell_33 | [
"text_plain_output_1.png"
] | piv1 = tr.loc[tr.answered_correctly != -1].groupby('content_id')['answered_correctly'].mean().reset_index()
piv1.columns = ['content_id', 'content_emb']
piv3 = tr.loc[tr.answered_correctly != -1].groupby('user_id')['answered_correctly'].mean().reset_index()
piv3.columns = ['user_id', 'user_emb'] | code |
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