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|---|---|---|---|
34135429/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'Low', color='orange') | code |
34135429/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'Volume', color='blue') | code |
34135429/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.head(5) | code |
34135429/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'Turnover', color='red') | code |
34135429/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.offline as py
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import seaborn as sns
color = sns.color_palette()
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import plotly.offline as py
from plotly import tools
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.express as px
nifty_50_df = pd.read_csv('../input/nifty-indices-dataset/NIFTY 50.csv', index_col='Date', parse_dates=['Date'])
nifty_50_df.isnull().sum()
nifty_50_df = nifty_50_df.fillna(method='ffill')
def plot_attribute(df, attritube ,start='2000', end='2020',color ='blue'):
fig, ax = plt.subplots(1, figsize=(20,5))
ax.plot(df[start:end].index, df[start:end][attritube],'tab:{}'.format(color))
ax.set_title("Nifty 50 stock {} from 2000 to 2020".format(attritube))
ax.axhline(y=df[start:end].describe()[attritube]["max"],linewidth=2, color='m')
ax.axhline(y=df[start:end].describe()[attritube]["min"],linewidth=2, color='c')
ax.axvline(x=df[attritube].idxmax(),linewidth=2, color='b')
ax.axvline(x=df[attritube].idxmin() ,linewidth=2, color='y')
ax.text(x=df[attritube].idxmax(),
y=df[start:end].describe()[attritube]["max"],
s='MAX',
horizontalalignment='right',
verticalalignment='bottom',
color='blue',
fontsize=20)
ax.text(x=df[attritube].idxmin(),
y=df[start:end].describe()[attritube]["min"],
s='MIN',
horizontalalignment='left',
verticalalignment='top',
color='red',
fontsize=20)
plt.show()
print("Max Value : ",df[start:end].describe()[attritube]["max"])
print("Min Value : ",df[start:end].describe()[attritube]["min"])
plot_attribute(nifty_50_df, 'High', color='green') | code |
17109357/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.head() | code |
17109357/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.columns
data.Species.unique() | code |
17109357/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
17109357/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt #drawing library
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.columns
data.Species.unique()
setosa = data[data.Species == 'Iris-setosa']
versicolor = data[data.Species == 'Iris-versicolor']
virginica = data[data.Species == 'Iris-virginica']
plt.plot(setosa.Id, setosa.PetalLengthCm, color='red', label='setosa')
plt.plot(versicolor.Id, versicolor.PetalLengthCm, color='green', label='versicolor')
plt.plot(virginica.Id, virginica.PetalLengthCm, color='blue', label='virginica')
plt.xlabel('Id')
plt.ylabel('PetalLengthCm')
plt.legend()
plt.show() | code |
17109357/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.info() | code |
17109357/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/Iris.csv')
data.columns | code |
130010265/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes_counts = df['diabetes'].value_counts()
correlation_matrix = df.corr(numeric_only=True)
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap')
plt.show() | code |
130010265/cell_9 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
plt.boxplot(df['bmi'])
plt.xlabel('BMI')
plt.title('Box Plot of BMI')
plt.show() | code |
130010265/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.describe() | code |
130010265/cell_2 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.metrics import classification_report, confusion_matrix | code |
130010265/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
plt.scatter(df['blood_glucose_level'], df['HbA1c_level'])
plt.xlabel('Blood Glucose Level')
plt.ylabel('HbA1c Level')
plt.title('Scatter Plot of Blood Glucose Level vs HbA1c Level')
plt.show() | code |
130010265/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.info() | code |
130010265/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
plt.hist(df['age'], bins=10, edgecolor='k')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Histogram of Age')
plt.show() | code |
130010265/cell_15 | [
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes_counts = df['diabetes'].value_counts()
correlation_matrix = df.corr(numeric_only=True)
df_diabet = df[df['diabetes'] == 1]
from pandas.plotting import scatter_matrix
scatter_matrix(df_diabet[['age', 'bmi', 'blood_glucose_level']], figsize=(8, 8))
plt.show() | code |
130010265/cell_16 | [
"image_output_1.png"
] | from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes_counts = df['diabetes'].value_counts()
correlation_matrix = df.corr(numeric_only=True)
df_diabet = df[df['diabetes'] == 1]
from pandas.plotting import scatter_matrix
sns.violinplot(x=df['diabetes'], y=df['age'])
plt.xlabel('Diabetes')
plt.ylabel('Age')
plt.title('Violin Plot of Diabetes and Age')
plt.show() | code |
130010265/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
plt.bar(gender_counts.index, gender_counts.values)
plt.xlabel('Gender')
plt.ylabel('Count')
plt.title('Bar Plot of Gender')
plt.show() | code |
130010265/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
gender_counts = df['gender'].value_counts()
diabetes_counts = df['diabetes'].value_counts()
plt.pie(diabetes_counts.values, labels=diabetes_counts.index, autopct='%1.1f%%')
plt.title('Pie Chart of Diabetes')
plt.show() | code |
130010265/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/diabetes-prediction-dataset/diabetes_prediction_dataset.csv')
df.head() | code |
16147703/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
zone_1 = pd.DataFrame()
zone_2 = pd.DataFrame()
zone_3 = pd.DataFrame()
for i in range(train.shape[0]):
if (train.at[i,'ZONE_CODE'] == 'ZONE01'):
zone_1 = zone_1.append(train.iloc[i])
elif (train.at[i,'ZONE_CODE'] == 'ZONE02'):
zone_2 = zone_2.append(train.iloc[i])
else:
zone_3 = zone_3.append(train.iloc[i])
for i in range(zone_1.shape[0]):
zone_1.at[i,'HOUR_ID'] = i
for i in range(zone_2.shape[0]):
zone_2.at[i,'HOUR_ID'] = i
for i in range(zone_3.shape[0]):
zone_3.at[i,'HOUR_ID'] = i
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line_1 = ax.plot(zone_1['HOUR_ID'], zone_1['MAX_USER'], label="zone 1")
line_2 = ax.plot(zone_2['HOUR_ID'], zone_2['MAX_USER'], label="zone 2")
line_3 = ax.plot(zone_3['HOUR_ID'], zone_3['MAX_USER'], label="zone 3")
ax.legend()
plt.show()
train = train.loc[train.UPDATE_TIME >= '2019-03-01']
min_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).min().reset_index()
max_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).max().reset_index()
groupby_mean = train.groupby(['ZONE_CODE', 'HOUR_ID']).mean().reset_index()
groupby_mean['smape_maxuser'] = (max_maxuser['MAX_USER'] - min_maxuser['MAX_USER']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_maxuser'] = min_maxuser['MAX_USER']
groupby_mean['smape_bandwidth'] = (max_maxuser['BANDWIDTH_TOTAL'] - min_maxuser['BANDWIDTH_TOTAL']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_bandwidth'] = min_maxuser['BANDWIDTH_TOTAL']
df_val = train.drop(['BANDWIDTH_TOTAL', 'MAX_USER'], axis=1).join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
df_test = test.join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
df_val.head() | code |
16147703/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
train.tail() | code |
16147703/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
print(train.shape)
print(test.shape) | code |
16147703/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
zone_1 = pd.DataFrame()
zone_2 = pd.DataFrame()
zone_3 = pd.DataFrame()
for i in range(train.shape[0]):
if (train.at[i,'ZONE_CODE'] == 'ZONE01'):
zone_1 = zone_1.append(train.iloc[i])
elif (train.at[i,'ZONE_CODE'] == 'ZONE02'):
zone_2 = zone_2.append(train.iloc[i])
else:
zone_3 = zone_3.append(train.iloc[i])
for i in range(zone_1.shape[0]):
zone_1.at[i,'HOUR_ID'] = i
for i in range(zone_2.shape[0]):
zone_2.at[i,'HOUR_ID'] = i
for i in range(zone_3.shape[0]):
zone_3.at[i,'HOUR_ID'] = i
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line_1 = ax.plot(zone_1['HOUR_ID'], zone_1['MAX_USER'], label="zone 1")
line_2 = ax.plot(zone_2['HOUR_ID'], zone_2['MAX_USER'], label="zone 2")
line_3 = ax.plot(zone_3['HOUR_ID'], zone_3['MAX_USER'], label="zone 3")
ax.legend()
plt.show()
train = train.loc[train.UPDATE_TIME >= '2019-03-01']
min_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).min().reset_index()
max_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).max().reset_index()
groupby_mean = train.groupby(['ZONE_CODE', 'HOUR_ID']).mean().reset_index()
groupby_mean['smape_maxuser'] = (max_maxuser['MAX_USER'] - min_maxuser['MAX_USER']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_maxuser'] = min_maxuser['MAX_USER']
groupby_mean['smape_bandwidth'] = (max_maxuser['BANDWIDTH_TOTAL'] - min_maxuser['BANDWIDTH_TOTAL']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_bandwidth'] = min_maxuser['BANDWIDTH_TOTAL']
df_val = train.drop(['BANDWIDTH_TOTAL', 'MAX_USER'], axis=1).join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
df_test = test.join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
THRESHOLD = 12
df_val.loc[(df_val['smape_bandwidth'] > THRESHOLD) & (df_val['min_of_bandwidth'] < 13), 'BANDWIDTH_TOTAL'] = np.nan
df_test.loc[(df_test['smape_bandwidth'] > THRESHOLD) & (df_test['min_of_bandwidth'] < 13), 'BANDWIDTH_TOTAL'] = np.nan
print(df_test['BANDWIDTH_TOTAL'].describe()) | code |
16147703/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
import os
print(os.listdir('../input')) | code |
16147703/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
zone_1 = pd.DataFrame()
zone_2 = pd.DataFrame()
zone_3 = pd.DataFrame()
for i in range(train.shape[0]):
if (train.at[i,'ZONE_CODE'] == 'ZONE01'):
zone_1 = zone_1.append(train.iloc[i])
elif (train.at[i,'ZONE_CODE'] == 'ZONE02'):
zone_2 = zone_2.append(train.iloc[i])
else:
zone_3 = zone_3.append(train.iloc[i])
for i in range(zone_1.shape[0]):
zone_1.at[i,'HOUR_ID'] = i
for i in range(zone_2.shape[0]):
zone_2.at[i,'HOUR_ID'] = i
for i in range(zone_3.shape[0]):
zone_3.at[i,'HOUR_ID'] = i
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line_1 = ax.plot(zone_1['HOUR_ID'], zone_1['MAX_USER'], label="zone 1")
line_2 = ax.plot(zone_2['HOUR_ID'], zone_2['MAX_USER'], label="zone 2")
line_3 = ax.plot(zone_3['HOUR_ID'], zone_3['MAX_USER'], label="zone 3")
ax.legend()
plt.show()
train = train.loc[train.UPDATE_TIME >= '2019-03-01']
min_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).min().reset_index()
max_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).max().reset_index()
groupby_mean = train.groupby(['ZONE_CODE', 'HOUR_ID']).mean().reset_index()
groupby_mean['smape_maxuser'] = (max_maxuser['MAX_USER'] - min_maxuser['MAX_USER']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_maxuser'] = min_maxuser['MAX_USER']
groupby_mean['smape_bandwidth'] = (max_maxuser['BANDWIDTH_TOTAL'] - min_maxuser['BANDWIDTH_TOTAL']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_bandwidth'] = min_maxuser['BANDWIDTH_TOTAL']
print(groupby_mean.shape) | code |
16147703/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
zone_1 = pd.DataFrame()
zone_2 = pd.DataFrame()
zone_3 = pd.DataFrame()
for i in range(train.shape[0]):
if (train.at[i,'ZONE_CODE'] == 'ZONE01'):
zone_1 = zone_1.append(train.iloc[i])
elif (train.at[i,'ZONE_CODE'] == 'ZONE02'):
zone_2 = zone_2.append(train.iloc[i])
else:
zone_3 = zone_3.append(train.iloc[i])
for i in range(zone_1.shape[0]):
zone_1.at[i,'HOUR_ID'] = i
for i in range(zone_2.shape[0]):
zone_2.at[i,'HOUR_ID'] = i
for i in range(zone_3.shape[0]):
zone_3.at[i,'HOUR_ID'] = i
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line_1 = ax.plot(zone_1['HOUR_ID'], zone_1['MAX_USER'], label="zone 1")
line_2 = ax.plot(zone_2['HOUR_ID'], zone_2['MAX_USER'], label="zone 2")
line_3 = ax.plot(zone_3['HOUR_ID'], zone_3['MAX_USER'], label="zone 3")
ax.legend()
plt.show()
train = train.loc[train.UPDATE_TIME >= '2019-03-01']
min_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).min().reset_index()
max_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).max().reset_index()
groupby_mean = train.groupby(['ZONE_CODE', 'HOUR_ID']).mean().reset_index()
groupby_mean['smape_maxuser'] = (max_maxuser['MAX_USER'] - min_maxuser['MAX_USER']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_maxuser'] = min_maxuser['MAX_USER']
groupby_mean['smape_bandwidth'] = (max_maxuser['BANDWIDTH_TOTAL'] - min_maxuser['BANDWIDTH_TOTAL']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_bandwidth'] = min_maxuser['BANDWIDTH_TOTAL']
df_val = train.drop(['BANDWIDTH_TOTAL', 'MAX_USER'], axis=1).join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
df_test = test.join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
print(df_val.shape) | code |
16147703/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
train.head() | code |
16147703/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
zone_1 = pd.DataFrame()
zone_2 = pd.DataFrame()
zone_3 = pd.DataFrame()
for i in range(train.shape[0]):
if (train.at[i,'ZONE_CODE'] == 'ZONE01'):
zone_1 = zone_1.append(train.iloc[i])
elif (train.at[i,'ZONE_CODE'] == 'ZONE02'):
zone_2 = zone_2.append(train.iloc[i])
else:
zone_3 = zone_3.append(train.iloc[i])
for i in range(zone_1.shape[0]):
zone_1.at[i,'HOUR_ID'] = i
for i in range(zone_2.shape[0]):
zone_2.at[i,'HOUR_ID'] = i
for i in range(zone_3.shape[0]):
zone_3.at[i,'HOUR_ID'] = i
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line_1 = ax.plot(zone_1['HOUR_ID'], zone_1['MAX_USER'], label="zone 1")
line_2 = ax.plot(zone_2['HOUR_ID'], zone_2['MAX_USER'], label="zone 2")
line_3 = ax.plot(zone_3['HOUR_ID'], zone_3['MAX_USER'], label="zone 3")
ax.legend()
plt.show()
train = train.loc[train.UPDATE_TIME >= '2019-03-01']
min_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).min().reset_index()
max_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).max().reset_index()
groupby_mean = train.groupby(['ZONE_CODE', 'HOUR_ID']).mean().reset_index()
groupby_mean['smape_maxuser'] = (max_maxuser['MAX_USER'] - min_maxuser['MAX_USER']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_maxuser'] = min_maxuser['MAX_USER']
groupby_mean['smape_bandwidth'] = (max_maxuser['BANDWIDTH_TOTAL'] - min_maxuser['BANDWIDTH_TOTAL']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_bandwidth'] = min_maxuser['BANDWIDTH_TOTAL']
df_val = train.drop(['BANDWIDTH_TOTAL', 'MAX_USER'], axis=1).join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
df_test = test.join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
df_val['smape_bandwidth'].describe() | code |
16147703/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
zone_1 = pd.DataFrame()
zone_2 = pd.DataFrame()
zone_3 = pd.DataFrame()
for i in range(train.shape[0]):
if (train.at[i,'ZONE_CODE'] == 'ZONE01'):
zone_1 = zone_1.append(train.iloc[i])
elif (train.at[i,'ZONE_CODE'] == 'ZONE02'):
zone_2 = zone_2.append(train.iloc[i])
else:
zone_3 = zone_3.append(train.iloc[i])
for i in range(zone_1.shape[0]):
zone_1.at[i,'HOUR_ID'] = i
for i in range(zone_2.shape[0]):
zone_2.at[i,'HOUR_ID'] = i
for i in range(zone_3.shape[0]):
zone_3.at[i,'HOUR_ID'] = i
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line_1 = ax.plot(zone_1['HOUR_ID'], zone_1['MAX_USER'], label="zone 1")
line_2 = ax.plot(zone_2['HOUR_ID'], zone_2['MAX_USER'], label="zone 2")
line_3 = ax.plot(zone_3['HOUR_ID'], zone_3['MAX_USER'], label="zone 3")
ax.legend()
plt.show()
train = train.loc[train.UPDATE_TIME >= '2019-03-01']
min_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).min().reset_index()
max_maxuser = train.groupby(['ZONE_CODE', 'HOUR_ID']).max().reset_index()
groupby_mean = train.groupby(['ZONE_CODE', 'HOUR_ID']).mean().reset_index()
groupby_mean['smape_maxuser'] = (max_maxuser['MAX_USER'] - min_maxuser['MAX_USER']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_maxuser'] = min_maxuser['MAX_USER']
groupby_mean['smape_bandwidth'] = (max_maxuser['BANDWIDTH_TOTAL'] - min_maxuser['BANDWIDTH_TOTAL']) / (max_maxuser['MAX_USER'] + min_maxuser['MAX_USER']) * 100
groupby_mean['min_of_bandwidth'] = min_maxuser['BANDWIDTH_TOTAL']
df_val = train.drop(['BANDWIDTH_TOTAL', 'MAX_USER'], axis=1).join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
df_test = test.join(groupby_mean.set_index(['ZONE_CODE', 'HOUR_ID']), on=['ZONE_CODE', 'HOUR_ID'])
THRESHOLD = 12
df_val.loc[(df_val['smape_bandwidth'] > THRESHOLD) & (df_val['min_of_bandwidth'] < 13), 'BANDWIDTH_TOTAL'] = np.nan
df_test.loc[(df_test['smape_bandwidth'] > THRESHOLD) & (df_test['min_of_bandwidth'] < 13), 'BANDWIDTH_TOTAL'] = np.nan
df_val.loc[(df_val['smape_maxuser'] > THRESHOLD) & (df_val['min_of_maxuser'] < 13), 'MAX_USER'] = np.nan
df_test.loc[(df_test['smape_maxuser'] > THRESHOLD) & (df_test['min_of_maxuser'] < 13), 'MAX_USER'] = np.nan
print(df_test['MAX_USER'].describe()) | code |
16147703/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test_id.csv')
train['UPDATE_TIME'] = train['UPDATE_TIME'].astype('datetime64[ns]')
test['UPDATE_TIME'] = test['UPDATE_TIME'].astype('datetime64[ns]')
zone_1 = pd.DataFrame()
zone_2 = pd.DataFrame()
zone_3 = pd.DataFrame()
for i in range(train.shape[0]):
if train.at[i, 'ZONE_CODE'] == 'ZONE01':
zone_1 = zone_1.append(train.iloc[i])
elif train.at[i, 'ZONE_CODE'] == 'ZONE02':
zone_2 = zone_2.append(train.iloc[i])
else:
zone_3 = zone_3.append(train.iloc[i])
for i in range(zone_1.shape[0]):
zone_1.at[i, 'HOUR_ID'] = i
for i in range(zone_2.shape[0]):
zone_2.at[i, 'HOUR_ID'] = i
for i in range(zone_3.shape[0]):
zone_3.at[i, 'HOUR_ID'] = i
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
line_1 = ax.plot(zone_1['HOUR_ID'], zone_1['MAX_USER'], label='zone 1')
line_2 = ax.plot(zone_2['HOUR_ID'], zone_2['MAX_USER'], label='zone 2')
line_3 = ax.plot(zone_3['HOUR_ID'], zone_3['MAX_USER'], label='zone 3')
ax.legend()
plt.show() | code |
122255317/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y-Axis', xlabel='X-Axis')
plt.show()
# create multiple subplots along rows
fig, ax = plt.subplots(nrows=2)
# create multiple subplots along columns
fig, ax = plt.subplots(ncols=2)
# create multiple subplots along rows and columns
fig, ax = plt.subplots(nrows=2,ncols=2)
plt.show()
# create multiple subplots without overlapping
fig, ax = plt.subplots(nrows=2,ncols=2)
plt.tight_layout() # avoid overlapping
plt.show()
# define the index of subplots
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0,0].set(title='Upper Left [0,0]')
axes[0,1].set(title='Upper Right [0,1]')
axes[1,0].set(title='Lower Left [1,0]')
axes[1,1].set(title='Lower Right [1,1]')
plt.tight_layout()
plt.show()
x = [1, 2, 3, 4, 5, 6, 7, 8]
y = [2, 3, 4, 5, 6, 7, 8, 9]
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))
ax[0, 0].plot(x, y)
ax[0, 0].bar(x, y)
ax[0, 1].scatter(x, y)
ax[1, 0].bar(x, y)
ax[1, 1].barh(x, y)
plt.show() | code |
122255317/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plt.show() | code |
122255317/cell_18 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y-Axis', xlabel='X-Axis')
plt.show()
# create multiple subplots along rows
fig, ax = plt.subplots(nrows=2)
# create multiple subplots along columns
fig, ax = plt.subplots(ncols=2)
# create multiple subplots along rows and columns
fig, ax = plt.subplots(nrows=2,ncols=2)
plt.show()
# create multiple subplots without overlapping
fig, ax = plt.subplots(nrows=2,ncols=2)
plt.tight_layout() # avoid overlapping
plt.show()
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].set(title='Upper Left [0,0]')
axes[0, 1].set(title='Upper Right [0,1]')
axes[1, 0].set(title='Lower Left [1,0]')
axes[1, 1].set(title='Lower Right [1,1]')
plt.tight_layout()
plt.show() | code |
122255317/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
fig, ax = plt.subplots(figsize=(10, 10))
plt.show() | code |
122255317/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y-Axis', xlabel='X-Axis')
plt.show()
# create multiple subplots along rows
fig, ax = plt.subplots(nrows=2)
fig, ax = plt.subplots(ncols=2) | code |
122255317/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y-Axis', xlabel='X-Axis')
plt.show()
# create multiple subplots along rows
fig, ax = plt.subplots(nrows=2)
# create multiple subplots along columns
fig, ax = plt.subplots(ncols=2)
fig, ax = plt.subplots(nrows=2, ncols=2)
plt.show() | code |
122255317/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y-Axis', xlabel='X-Axis')
plt.show()
# create multiple subplots along rows
fig, ax = plt.subplots(nrows=2)
# create multiple subplots along columns
fig, ax = plt.subplots(ncols=2)
# create multiple subplots along rows and columns
fig, ax = plt.subplots(nrows=2,ncols=2)
plt.show()
fig, ax = plt.subplots(nrows=2, ncols=2)
plt.tight_layout()
plt.show() | code |
122255317/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y-Axis', xlabel='X-Axis')
plt.show()
fig, ax = plt.subplots(nrows=2) | code |
122255317/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
fig, ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8], title='An Example Axes', ylabel='Y-Axis', xlabel='X-Axis')
plt.show() | code |
122255317/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
# create blank figure
fig, ax = plt.subplots()
plt.show()
# resize figure
fig, ax = plt.subplots(figsize=(10,10))
plt.show()
# set axis with xlim and ylim, title, labels
fig,ax = plt.subplots()
ax.set(xlim=[0.5, 4.5], ylim=[-2, 8],
title='An Example Axes',
ylabel='Y-Axis', xlabel='X-Axis')
plt.show()
plt.savefig('chart1.png')
plt.savefig('chart2.png', transparent=True) | code |
333413/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import pandas as pd
import xgboost as xgb
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
dup = phone.groupby('device_id').size()
dup = dup[dup > 1]
dup.shape
dup = phone.loc[phone.device_id.isin(dup.index)]
first = dup.groupby('device_id').first()
last = dup.groupby('device_id').last()
phone = phone.drop_duplicates('device_id', keep='first')
lebrand = LabelEncoder().fit(phone.phone_brand)
phone['brand'] = lebrand.transform(phone.phone_brand)
m = phone.phone_brand.str.cat(phone.device_model)
lemodel = LabelEncoder().fit(m)
phone['model'] = lemodel.transform(m)
phone['old_model'] = LabelEncoder().fit_transform(phone.device_model)
train = gatrain.merge(phone)
train['y'] = LabelEncoder().fit_transform(train['group'])
params = {'objective': 'multi:softprob', 'num_class': 12, 'booster': 'gbtree', 'max_depth': 8, 'eval_metric': 'mlogloss', 'eta': 0.02, 'silent': 1, 'alpha': 3}
def encode_cat(Xtrain, Xtest):
model_age = Xtrain.groupby(['model'])['age'].agg('mean')
brand_age = Xtrain.groupby(['brand'])['age'].agg('mean')
Xtest['model_age'] = Xtest['model'].map(model_age)
Xtrain['model_age'] = Xtrain['model'].map(model_age)
Xtest['brand_age'] = Xtest['brand'].map(brand_age)
Xtrain['brand_age'] = Xtrain['brand'].map(brand_age)
return (Xtrain[['brand', 'model', 'old_model']], Xtest[['brand', 'model', 'old_model']])
y = train['y']
kf = KFold(train.shape[0], n_folds=5, shuffle=True, random_state=1024)
pred = np.zeros((train.shape[0], 12))
for itrain, itest in kf:
Xtrain = train.ix[itrain,]
Xtest = train.ix[itest,]
ytrain, ytest = (y[itrain], y[itest])
Xtrain, Xtest = encode_cat(Xtrain, Xtest)
dtrain = xgb.DMatrix(Xtrain, label=ytrain)
dvalid = xgb.DMatrix(Xtest, label=ytest)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
gbm = xgb.train(params, dtrain, 600, evals=watchlist, early_stopping_rounds=25, verbose_eval=20)
temp_pred = gbm.predict(dvalid)
pred[itest, :] = temp_pred
print(log_loss(ytest, temp_pred)) | code |
333413/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
gatrain.head(3) | code |
333413/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import os
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
import xgboost as xgb | code |
333413/cell_11 | [
"text_html_output_1.png"
] | params = {'objective': 'multi:softprob', 'num_class': 12, 'booster': 'gbtree', 'max_depth': 8, 'eval_metric': 'mlogloss', 'eta': 0.02, 'silent': 1, 'alpha': 3} | code |
333413/cell_15 | [
"text_html_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import pandas as pd
import xgboost as xgb
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
dup = phone.groupby('device_id').size()
dup = dup[dup > 1]
dup.shape
dup = phone.loc[phone.device_id.isin(dup.index)]
first = dup.groupby('device_id').first()
last = dup.groupby('device_id').last()
phone = phone.drop_duplicates('device_id', keep='first')
lebrand = LabelEncoder().fit(phone.phone_brand)
phone['brand'] = lebrand.transform(phone.phone_brand)
m = phone.phone_brand.str.cat(phone.device_model)
lemodel = LabelEncoder().fit(m)
phone['model'] = lemodel.transform(m)
phone['old_model'] = LabelEncoder().fit_transform(phone.device_model)
train = gatrain.merge(phone)
train['y'] = LabelEncoder().fit_transform(train['group'])
params = {'objective': 'multi:softprob', 'num_class': 12, 'booster': 'gbtree', 'max_depth': 8, 'eval_metric': 'mlogloss', 'eta': 0.02, 'silent': 1, 'alpha': 3}
def encode_cat(Xtrain, Xtest):
model_age = Xtrain.groupby(['model'])['age'].agg('mean')
brand_age = Xtrain.groupby(['brand'])['age'].agg('mean')
Xtest['model_age'] = Xtest['model'].map(model_age)
Xtrain['model_age'] = Xtrain['model'].map(model_age)
Xtest['brand_age'] = Xtest['brand'].map(brand_age)
Xtrain['brand_age'] = Xtrain['brand'].map(brand_age)
return (Xtrain[['brand', 'model', 'old_model']], Xtest[['brand', 'model', 'old_model']])
y = train['y']
kf = KFold(train.shape[0], n_folds=5, shuffle=True, random_state=1024)
pred = np.zeros((train.shape[0], 12))
for itrain, itest in kf:
Xtrain = train.ix[itrain,]
Xtest = train.ix[itest,]
ytrain, ytest = (y[itrain], y[itest])
Xtrain, Xtest = encode_cat(Xtrain, Xtest)
dtrain = xgb.DMatrix(Xtrain, label=ytrain)
dvalid = xgb.DMatrix(Xtest, label=ytest)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
gbm = xgb.train(params, dtrain, 600, evals=watchlist, early_stopping_rounds=25, verbose_eval=20)
temp_pred = gbm.predict(dvalid)
pred[itest, :] = temp_pred
pred.shape | code |
333413/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
phone.head(3) | code |
333413/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import pandas as pd
import xgboost as xgb
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
dup = phone.groupby('device_id').size()
dup = dup[dup > 1]
dup.shape
dup = phone.loc[phone.device_id.isin(dup.index)]
first = dup.groupby('device_id').first()
last = dup.groupby('device_id').last()
phone = phone.drop_duplicates('device_id', keep='first')
lebrand = LabelEncoder().fit(phone.phone_brand)
phone['brand'] = lebrand.transform(phone.phone_brand)
m = phone.phone_brand.str.cat(phone.device_model)
lemodel = LabelEncoder().fit(m)
phone['model'] = lemodel.transform(m)
phone['old_model'] = LabelEncoder().fit_transform(phone.device_model)
train = gatrain.merge(phone)
train['y'] = LabelEncoder().fit_transform(train['group'])
params = {'objective': 'multi:softprob', 'num_class': 12, 'booster': 'gbtree', 'max_depth': 8, 'eval_metric': 'mlogloss', 'eta': 0.02, 'silent': 1, 'alpha': 3}
def encode_cat(Xtrain, Xtest):
model_age = Xtrain.groupby(['model'])['age'].agg('mean')
brand_age = Xtrain.groupby(['brand'])['age'].agg('mean')
Xtest['model_age'] = Xtest['model'].map(model_age)
Xtrain['model_age'] = Xtrain['model'].map(model_age)
Xtest['brand_age'] = Xtest['brand'].map(brand_age)
Xtrain['brand_age'] = Xtrain['brand'].map(brand_age)
return (Xtrain[['brand', 'model', 'old_model']], Xtest[['brand', 'model', 'old_model']])
y = train['y']
kf = KFold(train.shape[0], n_folds=5, shuffle=True, random_state=1024)
pred = np.zeros((train.shape[0], 12))
for itrain, itest in kf:
Xtrain = train.ix[itrain,]
Xtest = train.ix[itest,]
ytrain, ytest = (y[itrain], y[itest])
Xtrain, Xtest = encode_cat(Xtrain, Xtest)
dtrain = xgb.DMatrix(Xtrain, label=ytrain)
dvalid = xgb.DMatrix(Xtest, label=ytest)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]
gbm = xgb.train(params, dtrain, 600, evals=watchlist, early_stopping_rounds=25, verbose_eval=20)
temp_pred = gbm.predict(dvalid)
pred[itest, :] = temp_pred
log_loss(train['y'].values.tolist(), pred) | code |
333413/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
gatrain = pd.read_csv('../input/gender_age_train.csv')
gatest = pd.read_csv('../input/gender_age_test.csv')
dup = phone.groupby('device_id').size()
dup = dup[dup > 1]
dup.shape
dup = phone.loc[phone.device_id.isin(dup.index)]
first = dup.groupby('device_id').first()
last = dup.groupby('device_id').last()
phone = phone.drop_duplicates('device_id', keep='first')
lebrand = LabelEncoder().fit(phone.phone_brand)
phone['brand'] = lebrand.transform(phone.phone_brand)
m = phone.phone_brand.str.cat(phone.device_model)
lemodel = LabelEncoder().fit(m)
phone['model'] = lemodel.transform(m)
phone['old_model'] = LabelEncoder().fit_transform(phone.device_model)
train = gatrain.merge(phone)
train['y'] = LabelEncoder().fit_transform(train['group'])
train.head() | code |
333413/cell_12 | [
"text_html_output_1.png"
] | def encode_cat(Xtrain, Xtest):
model_age = Xtrain.groupby(['model'])['age'].agg('mean')
brand_age = Xtrain.groupby(['brand'])['age'].agg('mean')
Xtest['model_age'] = Xtest['model'].map(model_age)
Xtrain['model_age'] = Xtrain['model'].map(model_age)
Xtest['brand_age'] = Xtest['brand'].map(brand_age)
Xtrain['brand_age'] = Xtrain['brand'].map(brand_age)
return (Xtrain[['brand', 'model', 'old_model']], Xtest[['brand', 'model', 'old_model']]) | code |
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] | import pandas as pd
import pandas as pd
phone = pd.read_csv('../input/phone_brand_device_model.csv', encoding='utf-8')
dup = phone.groupby('device_id').size()
dup = dup[dup > 1]
dup.shape | code |
105204911/cell_3 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip uninstall -q -y transformers | code |
105204911/cell_10 | [
"text_plain_output_1.png"
] | from torch import nn
from transformers import AutoModel
import torch
import torch
from torch import nn
from transformers import AutoModel
@torch.no_grad()
def turn_off_dropout(module: nn.Module) -> None:
if isinstance(module, nn.Dropout):
module.p = 0.0
model_path = 'distilbert-base-uncased'
model = AutoModel.from_pretrained(model_path)
model.apply(turn_off_dropout)
model | code |
50222260/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(x_train, y_train)
classifier.score(x_test, y_test)
y_predicted = classifier.predict(x_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_predicted)
cm | code |
50222260/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sm
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(x_train, y_train)
classifier.score(x_test, y_test)
y_predicted = classifier.predict(x_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_predicted)
cm
plt.figure(figsize=(10, 7))
sm.heatmap(cm, annot=True)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show() | code |
50222260/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(x_train, y_train)
classifier.score(x_test, y_test) | code |
50222260/cell_8 | [
"image_output_1.png"
] | from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
for i in range(5):
plt.matshow(digits.images[i]) | code |
50222260/cell_16 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier()
classifier.fit(x_train, y_train) | code |
50222260/cell_10 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
x = pd.DataFrame(digits.data)
x | code |
50222260/cell_12 | [
"text_html_output_1.png"
] | from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_digits
digits = load_digits()
plt.gray()
x = pd.DataFrame(digits.data)
x
y = pd.DataFrame(digits.target)
y | code |
106202736/cell_3 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import numpy as np
import cv2
from numpy import save
image_arr_new = np.load('../input/imagearray201/image_array_20_1.npy')
image_array = []
for i in image_arr_new:
image_array.append(cv2.resize(i, (227, 227)))
image_array = np.array(image_array)
image_array.shape | code |
121154614/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd #for reading & storing data, pre-processing
import pandas as pd # To use dataframes
train = pd.read_csv('/kaggle/input/walmart-sales-forecast/train.csv')
stores = pd.read_csv('/kaggle/input/walmart-sales-forecast/stores.csv')
features = pd.read_csv('/kaggle/input/walmart-sales-forecast/features.csv')
dataset = train
indexedDataset = dataset.set_index(['Date'], inplace=True)
dataset.drop(['Store', 'Dept', 'IsHoliday'], axis=1, inplace=True)
dataset | code |
121154614/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import statsmodels.api as sm
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
from datetime import datetime
import numpy as np #for numerical computations like log,exp,sqrt etc
import pandas as pd #for reading & storing data, pre-processing
import matplotlib.pylab as plt #for visualization
#for making sure matplotlib plots are generated in Jupyter notebook itself
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.stattools import acf, pacf
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.tsa.arima_model import ARIMA
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 10, 6
import numpy as np # To use np.arrays
import pandas as pd # To use dataframes
from pandas.plotting import autocorrelation_plot as auto_corr
# To plot
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
#For date-time
import math
from datetime import datetime
from datetime import timedelta
# Another imports if needs
import itertools
import statsmodels.api as sm
import statsmodels.tsa.api as smt
import statsmodels.formula.api as smf
from sklearn.model_selection import train_test_split
from statsmodels.tsa.seasonal import seasonal_decompose as season
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.metrics import accuracy_score, balanced_accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn import preprocessing
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.stattools import adfuller, acf, pacf
from statsmodels.tsa.arima_model import ARIMA
!pip install pmdarima
from pmdarima.utils import decomposed_plot
from pmdarima.arima import decompose
from pmdarima import auto_arima | code |
121154614/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd #for reading & storing data, pre-processing
import pandas as pd # To use dataframes
train = pd.read_csv('/kaggle/input/walmart-sales-forecast/train.csv')
stores = pd.read_csv('/kaggle/input/walmart-sales-forecast/stores.csv')
features = pd.read_csv('/kaggle/input/walmart-sales-forecast/features.csv')
train.set_index('Date', inplace=True) | code |
121154614/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd #for reading & storing data, pre-processing
import pandas as pd # To use dataframes
train = pd.read_csv('/kaggle/input/walmart-sales-forecast/train.csv')
stores = pd.read_csv('/kaggle/input/walmart-sales-forecast/stores.csv')
features = pd.read_csv('/kaggle/input/walmart-sales-forecast/features.csv')
dataset = train
indexedDataset = dataset.set_index(['Date'], inplace=True)
dataset.drop(['Store', 'Dept', 'IsHoliday'], axis=1, inplace=True)
dataset | code |
106208686/cell_13 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from sklearn.preprocessing import MinMaxScaler
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import numpy as np
import pandas
import tensorflow as tf
import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense
from keras_tuner import HyperModel
import kerastuner as kt
from tcn import TCN
def tcnModel(trainData, validateData, numOfObservedRecords: int, numOfPredictingRecords: int):
hyperModel = TCNModel(numOfObservedRecords=numOfObservedRecords, numOfPredictingRecords=numOfPredictingRecords, numOfFeatures=trainData.shape[2])
bayesianTuner = kt.tuners.BayesianOptimization(hyperModel, objective='mse', max_trials=3, project_name='kerastuner_bayesian_poc', executions_per_trial=5, overwrite=True)
bayesianTuner.search(trainData, validateData, epochs=100, validation_split=0.2, verbose=0)
return bayesianTuner.get_best_models(num_models=1)[0]
class TCNModel(HyperModel):
def __init__(self, numOfObservedRecords, numOfPredictingRecords, numOfFeatures):
self.numOfObservedRecords = numOfObservedRecords
self.numOfPredictingRecords = numOfPredictingRecords
self.numOfFeatures = numOfFeatures
def build(self, params):
model = keras.Sequential()
model.add(TCN(input_shape=(self.numOfObservedRecords, 1), kernel_size=params.Int('units', min_value=2, max_value=8, step=1), use_skip_connections=params.Boolean('use_skip_connections'), use_batch_norm=False, use_weight_norm=False, use_layer_norm=True, dropout_rate=params.Float('drop_out', 0, 0.5, 0.1), nb_filters=params.Int('units', min_value=32, max_value=512, step=32)))
model.add(Dense(self.numOfPredictingRecords, activation=params.Choice('dense_activation', values=['relu', 'tanh', 'sigmoid'], default='relu')))
model.compile(loss='mse', metrics=['mse'], optimizer=tf.keras.optimizers.Adam(params.Choice('learning_rate', values=[0.01, 0.001, 0.0001])))
return model
from sklearn.preprocessing import MinMaxScaler
def scale3DArray(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))) -> np.ndarray:
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return scaledArr
def scale3DArrayReturningScaler(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))):
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return (scaledArr, scaler)
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
data = list(data['value'])[:10200]
data_train = data[:int(len(data) * 0.8)]
data_test = data[int(len(data) * 0.8):]
O_data_test = data_test
minmaxSc = MinMaxScaler()
data_train = np.array(data_train)
data_train = np.reshape(data_train, (int(data_train.shape[0] / 120), 120))
data_train = minmaxSc.fit_transform(data_train)
train, validate = np.array_split(data_train, [72], 1)
train = np.reshape(train, (train.shape[0], train.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
model = tcnModel(train, validate, 72, 48) | code |
106208686/cell_4 | [
"text_plain_output_1.png"
] | pip install keras-tcn | code |
106208686/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
data = list(data['value'])[:10200]
data_train = data[:int(len(data) * 0.8)]
data_test = data[int(len(data) * 0.8):]
print('len train ', len(data_train))
print('len test ', len(data_test))
O_data_test = data_test | code |
106208686/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import keras
from keras.layers import Dense
import os
import pandas
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106208686/cell_7 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import tensorflow as tf
import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense
from keras_tuner import HyperModel
import kerastuner as kt
from tcn import TCN
def tcnModel(trainData, validateData, numOfObservedRecords: int, numOfPredictingRecords: int):
hyperModel = TCNModel(numOfObservedRecords=numOfObservedRecords, numOfPredictingRecords=numOfPredictingRecords, numOfFeatures=trainData.shape[2])
bayesianTuner = kt.tuners.BayesianOptimization(hyperModel, objective='mse', max_trials=3, project_name='kerastuner_bayesian_poc', executions_per_trial=5, overwrite=True)
bayesianTuner.search(trainData, validateData, epochs=100, validation_split=0.2, verbose=0)
return bayesianTuner.get_best_models(num_models=1)[0]
class TCNModel(HyperModel):
def __init__(self, numOfObservedRecords, numOfPredictingRecords, numOfFeatures):
self.numOfObservedRecords = numOfObservedRecords
self.numOfPredictingRecords = numOfPredictingRecords
self.numOfFeatures = numOfFeatures
def build(self, params):
model = keras.Sequential()
model.add(TCN(input_shape=(self.numOfObservedRecords, 1), kernel_size=params.Int('units', min_value=2, max_value=8, step=1), use_skip_connections=params.Boolean('use_skip_connections'), use_batch_norm=False, use_weight_norm=False, use_layer_norm=True, dropout_rate=params.Float('drop_out', 0, 0.5, 0.1), nb_filters=params.Int('units', min_value=32, max_value=512, step=32)))
model.add(Dense(self.numOfPredictingRecords, activation=params.Choice('dense_activation', values=['relu', 'tanh', 'sigmoid'], default='relu')))
model.compile(loss='mse', metrics=['mse'], optimizer=tf.keras.optimizers.Adam(params.Choice('learning_rate', values=[0.01, 0.001, 0.0001])))
return model | code |
106208686/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from sklearn.preprocessing import MinMaxScaler
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import numpy as np
import pandas
import tensorflow as tf
import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense
from keras_tuner import HyperModel
import kerastuner as kt
from tcn import TCN
def tcnModel(trainData, validateData, numOfObservedRecords: int, numOfPredictingRecords: int):
hyperModel = TCNModel(numOfObservedRecords=numOfObservedRecords, numOfPredictingRecords=numOfPredictingRecords, numOfFeatures=trainData.shape[2])
bayesianTuner = kt.tuners.BayesianOptimization(hyperModel, objective='mse', max_trials=3, project_name='kerastuner_bayesian_poc', executions_per_trial=5, overwrite=True)
bayesianTuner.search(trainData, validateData, epochs=100, validation_split=0.2, verbose=0)
return bayesianTuner.get_best_models(num_models=1)[0]
class TCNModel(HyperModel):
def __init__(self, numOfObservedRecords, numOfPredictingRecords, numOfFeatures):
self.numOfObservedRecords = numOfObservedRecords
self.numOfPredictingRecords = numOfPredictingRecords
self.numOfFeatures = numOfFeatures
def build(self, params):
model = keras.Sequential()
model.add(TCN(input_shape=(self.numOfObservedRecords, 1), kernel_size=params.Int('units', min_value=2, max_value=8, step=1), use_skip_connections=params.Boolean('use_skip_connections'), use_batch_norm=False, use_weight_norm=False, use_layer_norm=True, dropout_rate=params.Float('drop_out', 0, 0.5, 0.1), nb_filters=params.Int('units', min_value=32, max_value=512, step=32)))
model.add(Dense(self.numOfPredictingRecords, activation=params.Choice('dense_activation', values=['relu', 'tanh', 'sigmoid'], default='relu')))
model.compile(loss='mse', metrics=['mse'], optimizer=tf.keras.optimizers.Adam(params.Choice('learning_rate', values=[0.01, 0.001, 0.0001])))
return model
from sklearn.preprocessing import MinMaxScaler
def scale3DArray(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))) -> np.ndarray:
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return scaledArr
def scale3DArrayReturningScaler(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))):
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return (scaledArr, scaler)
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
data = list(data['value'])[:10200]
data_train = data[:int(len(data) * 0.8)]
data_test = data[int(len(data) * 0.8):]
O_data_test = data_test
minmaxSc = MinMaxScaler()
data_train = np.array(data_train)
data_train = np.reshape(data_train, (int(data_train.shape[0] / 120), 120))
data_train = minmaxSc.fit_transform(data_train)
train, validate = np.array_split(data_train, [72], 1)
train = np.reshape(train, (train.shape[0], train.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
model = tcnModel(train, validate, 72, 48)
data_test = np.array(data_test)
data_test = np.reshape(data_test, (int(data_test.shape[0] / 120), 120))
data_test = minmaxSc.transform(data_test)
test, validate = np.array_split(data_test, [72], 1)
test = np.reshape(test, (test.shape[0], test.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
test_predict = np.array(model.predict(test))
print(test_predict.shape)
test_predict = np.reshape(test_predict, (test_predict.shape[0], test_predict.shape[1], 1))
print(test_predict.shape)
test_predict_full = np.concatenate((test, test_predict), axis=1)
print(test_predict_full.shape)
test_predict_full = np.squeeze(test_predict_full)
print(test_predict_full.shape)
test_predict_full = minmaxSc.inverse_transform(test_predict_full)
test_predict_full = list(test_predict_full.flatten())
print(len(test_predict_full)) | code |
106208686/cell_16 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from keras.layers import Dense
from keras_tuner import HyperModel
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import MinMaxScaler
from tcn import TCN
from tensorflow import keras
import keras
import kerastuner as kt
import numpy as np
import pandas
import tensorflow as tf
import tensorflow as tf
from tensorflow import keras
from keras.layers import Dense
from keras_tuner import HyperModel
import kerastuner as kt
from tcn import TCN
def tcnModel(trainData, validateData, numOfObservedRecords: int, numOfPredictingRecords: int):
hyperModel = TCNModel(numOfObservedRecords=numOfObservedRecords, numOfPredictingRecords=numOfPredictingRecords, numOfFeatures=trainData.shape[2])
bayesianTuner = kt.tuners.BayesianOptimization(hyperModel, objective='mse', max_trials=3, project_name='kerastuner_bayesian_poc', executions_per_trial=5, overwrite=True)
bayesianTuner.search(trainData, validateData, epochs=100, validation_split=0.2, verbose=0)
return bayesianTuner.get_best_models(num_models=1)[0]
class TCNModel(HyperModel):
def __init__(self, numOfObservedRecords, numOfPredictingRecords, numOfFeatures):
self.numOfObservedRecords = numOfObservedRecords
self.numOfPredictingRecords = numOfPredictingRecords
self.numOfFeatures = numOfFeatures
def build(self, params):
model = keras.Sequential()
model.add(TCN(input_shape=(self.numOfObservedRecords, 1), kernel_size=params.Int('units', min_value=2, max_value=8, step=1), use_skip_connections=params.Boolean('use_skip_connections'), use_batch_norm=False, use_weight_norm=False, use_layer_norm=True, dropout_rate=params.Float('drop_out', 0, 0.5, 0.1), nb_filters=params.Int('units', min_value=32, max_value=512, step=32)))
model.add(Dense(self.numOfPredictingRecords, activation=params.Choice('dense_activation', values=['relu', 'tanh', 'sigmoid'], default='relu')))
model.compile(loss='mse', metrics=['mse'], optimizer=tf.keras.optimizers.Adam(params.Choice('learning_rate', values=[0.01, 0.001, 0.0001])))
return model
from sklearn.preprocessing import MinMaxScaler
def scale3DArray(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))) -> np.ndarray:
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return scaledArr
def scale3DArrayReturningScaler(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))):
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return (scaledArr, scaler)
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
data = list(data['value'])[:10200]
data_train = data[:int(len(data) * 0.8)]
data_test = data[int(len(data) * 0.8):]
O_data_test = data_test
minmaxSc = MinMaxScaler()
data_train = np.array(data_train)
data_train = np.reshape(data_train, (int(data_train.shape[0] / 120), 120))
data_train = minmaxSc.fit_transform(data_train)
train, validate = np.array_split(data_train, [72], 1)
train = np.reshape(train, (train.shape[0], train.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
model = tcnModel(train, validate, 72, 48)
data_test = np.array(data_test)
data_test = np.reshape(data_test, (int(data_test.shape[0] / 120), 120))
data_test = minmaxSc.transform(data_test)
test, validate = np.array_split(data_test, [72], 1)
test = np.reshape(test, (test.shape[0], test.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
test_predict = np.array(model.predict(test))
test_predict = np.reshape(test_predict, (test_predict.shape[0], test_predict.shape[1], 1))
test_predict_full = np.concatenate((test, test_predict), axis=1)
test_predict_full = np.squeeze(test_predict_full)
test_predict_full = minmaxSc.inverse_transform(test_predict_full)
test_predict_full = list(test_predict_full.flatten())
from sklearn.metrics import mean_squared_error, r2_score
r2 = round(r2_score(O_data_test, test_predict_full), 3)
rmse = round(np.sqrt(mean_squared_error(O_data_test, test_predict_full)), 3)
print(r2)
print(rmse) | code |
106208686/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas
from sklearn.preprocessing import MinMaxScaler
def scale3DArray(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))) -> np.ndarray:
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return scaledArr
def scale3DArrayReturningScaler(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))):
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return (scaledArr, scaler)
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
data = list(data['value'])[:10200]
data_train = data[:int(len(data) * 0.8)]
data_test = data[int(len(data) * 0.8):]
O_data_test = data_test
minmaxSc = MinMaxScaler()
data_train = np.array(data_train)
data_train = np.reshape(data_train, (int(data_train.shape[0] / 120), 120))
data_train = minmaxSc.fit_transform(data_train)
train, validate = np.array_split(data_train, [72], 1)
train = np.reshape(train, (train.shape[0], train.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
data_test = np.array(data_test)
print(data_test.shape)
data_test = np.reshape(data_test, (int(data_test.shape[0] / 120), 120))
print(data_test.shape)
data_test = minmaxSc.transform(data_test)
print(data_test.shape)
test, validate = np.array_split(data_test, [72], 1)
print(test.shape)
print(validate.shape)
test = np.reshape(test, (test.shape[0], test.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
print(test.shape)
print(validate.shape) | code |
106208686/cell_10 | [
"text_plain_output_1.png"
] | import pandas
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
print(data.head())
print(len(data))
data = list(data['value'])[:10200]
print(len(data)) | code |
106208686/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import numpy as np
import pandas
from sklearn.preprocessing import MinMaxScaler
def scale3DArray(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))) -> np.ndarray:
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return scaledArr
def scale3DArrayReturningScaler(arr: np.ndarray, scaler: any=MinMaxScaler(feature_range=(-1, 1))):
scaledArr = np.reshape(arr, (arr.shape[0], arr.shape[1] * arr.shape[2]))
scaledArr = scaler.fit_transform(scaledArr)
scaledArr = np.reshape(scaledArr, tuple(arr.shape))
return (scaledArr, scaler)
data = pandas.read_csv('../input/nyc-taxi-traffic/dataset.csv')
data = list(data['value'])[:10200]
data_train = data[:int(len(data) * 0.8)]
data_test = data[int(len(data) * 0.8):]
O_data_test = data_test
minmaxSc = MinMaxScaler()
data_train = np.array(data_train)
print(data_train.shape)
data_train = np.reshape(data_train, (int(data_train.shape[0] / 120), 120))
print(data_train.shape)
data_train = minmaxSc.fit_transform(data_train)
print(data_train.shape)
train, validate = np.array_split(data_train, [72], 1)
print(train.shape)
print(validate.shape)
train = np.reshape(train, (train.shape[0], train.shape[1], 1))
validate = np.reshape(validate, (validate.shape[0], validate.shape[1], 1))
print(train.shape)
print(validate.shape) | code |
16167156/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
data_lokasi.info() | code |
16167156/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
profil_karyawan.info() | code |
16167156/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
print(data_lokasi.head())
print('ukuran data: ' + str(data_lokasi.shape)) | code |
16167156/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data_lokasi = pd.read_csv('../input/catatan_lokasi.csv')
profil_karyawan = pd.read_csv('../input/data_profil.csv')
print(profil_karyawan.head())
print('ukuran data: ' + str(profil_karyawan.shape)) | code |
16130176/cell_13 | [
"application_vnd.jupyter.stderr_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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
atomic_radius = {'H': 0.38, 'C': 0.77, 'N': 0.75, 'O': 0.73, 'F': 0.71, np.nan: 0}
atomic_number = {'H': 1, 'C': 6, 'N': 7, 'O': 8, 'F': 9, np.nan: 0}
atomic_mass = {'H': 1.0079, 'C': 12.0107, 'N': 14.0067, 'O': 15.9994, 'F': 18.9984, np.nan: 0}
vanderwaalsradius = {'H': 120, 'C': 185, 'N': 154, 'O': 140, 'F': 135, np.nan: 0}
covalenzradius = {'H': 30, 'C': 77, 'N': 70, 'O': 66, 'F': 58, np.nan: 0}
electronegativity = {'H': 2.2, 'C': 2.55, 'N': 3.04, 'O': 3.44, 'F': 3.98, np.nan: 0}
ionization_energy = {'H': 13.5984, 'C': 11.2603, 'N': 14.5341, 'O': 13.6181, 'F': 17.4228, np.nan: np.inf}
def atom_props(df, suffix):
df['atomic_radius' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_radius[x])
df['atomic_protons' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_number[x])
df['atomic_mass' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_mass[x])
df['vanderwaalsradius' + suffix] = df['atom_' + suffix].apply(lambda x: vanderwaalsradius[x])
df['covalenzradius' + suffix] = df['atom_' + suffix].apply(lambda x: covalenzradius[x])
df['electronegativity' + suffix] = df['atom_' + suffix].apply(lambda x: electronegativity[x])
df['ionization_energy' + suffix] = df['atom_' + suffix].apply(lambda x: ionization_energy[x])
return df
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
atom_cnt = structures['molecule_name'].value_counts().reset_index(level=0)
atom_cnt.rename({'index': 'molecule_name', 'molecule_name': 'atom_count'}, axis=1, inplace=True)
train = pd.merge(train, atom_cnt, how='left', on='molecule_name')
test = pd.merge(test, atom_cnt, how='left', on='molecule_name')
del atom_cnt
def lr(df):
df['atom_index_0l'] = df['atom_index_0'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_0', 'atom_count']]
df['atom_index_0r'] = tmp.apply(lambda row: min(row['atom_index_0'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0r'}, axis=1)
df['atom_index_1l'] = df['atom_index_1'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_1', 'atom_count']]
df['atom_index_1r'] = tmp.apply(lambda row: min(row['atom_index_1'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1r'}, axis=1)
return df
train = lr(train)
test = lr(test)
train = atom_props(train, '0')
train = atom_props(train, '0l')
train = atom_props(train, '0r')
train = atom_props(train, '1')
train = atom_props(train, '1l')
train = atom_props(train, '1r')
test = atom_props(test, '0')
test = atom_props(test, '0l')
test = atom_props(test, '0r')
test = atom_props(test, '1')
test = atom_props(test, '1l')
test = atom_props(test, '1r')
train.drop(['atom_index_x', 'atom_index_y'], axis=1, inplace=True)
test.drop(['atom_index_x', 'atom_index_y'], axis=1, inplace=True) | code |
16130176/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
atom_cnt = structures['molecule_name'].value_counts().reset_index(level=0)
atom_cnt.rename({'index': 'molecule_name', 'molecule_name': 'atom_count'}, axis=1, inplace=True)
train = pd.merge(train, atom_cnt, how='left', on='molecule_name')
test = pd.merge(test, atom_cnt, how='left', on='molecule_name')
del atom_cnt | code |
16130176/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16130176/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
atom_cnt = structures['molecule_name'].value_counts().reset_index(level=0)
atom_cnt.rename({'index': 'molecule_name', 'molecule_name': 'atom_count'}, axis=1, inplace=True)
train = pd.merge(train, atom_cnt, how='left', on='molecule_name')
test = pd.merge(test, atom_cnt, how='left', on='molecule_name')
del atom_cnt
def lr(df):
df['atom_index_0l'] = df['atom_index_0'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_0', 'atom_count']]
df['atom_index_0r'] = tmp.apply(lambda row: min(row['atom_index_0'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0r'}, axis=1)
df['atom_index_1l'] = df['atom_index_1'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_1', 'atom_count']]
df['atom_index_1r'] = tmp.apply(lambda row: min(row['atom_index_1'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1r'}, axis=1)
return df
train = lr(train)
test = lr(test) | code |
16130176/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1) | code |
16130176/cell_15 | [
"application_vnd.jupyter.stderr_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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
atomic_radius = {'H': 0.38, 'C': 0.77, 'N': 0.75, 'O': 0.73, 'F': 0.71, np.nan: 0}
atomic_number = {'H': 1, 'C': 6, 'N': 7, 'O': 8, 'F': 9, np.nan: 0}
atomic_mass = {'H': 1.0079, 'C': 12.0107, 'N': 14.0067, 'O': 15.9994, 'F': 18.9984, np.nan: 0}
vanderwaalsradius = {'H': 120, 'C': 185, 'N': 154, 'O': 140, 'F': 135, np.nan: 0}
covalenzradius = {'H': 30, 'C': 77, 'N': 70, 'O': 66, 'F': 58, np.nan: 0}
electronegativity = {'H': 2.2, 'C': 2.55, 'N': 3.04, 'O': 3.44, 'F': 3.98, np.nan: 0}
ionization_energy = {'H': 13.5984, 'C': 11.2603, 'N': 14.5341, 'O': 13.6181, 'F': 17.4228, np.nan: np.inf}
def atom_props(df, suffix):
df['atomic_radius' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_radius[x])
df['atomic_protons' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_number[x])
df['atomic_mass' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_mass[x])
df['vanderwaalsradius' + suffix] = df['atom_' + suffix].apply(lambda x: vanderwaalsradius[x])
df['covalenzradius' + suffix] = df['atom_' + suffix].apply(lambda x: covalenzradius[x])
df['electronegativity' + suffix] = df['atom_' + suffix].apply(lambda x: electronegativity[x])
df['ionization_energy' + suffix] = df['atom_' + suffix].apply(lambda x: ionization_energy[x])
return df
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
atom_cnt = structures['molecule_name'].value_counts().reset_index(level=0)
atom_cnt.rename({'index': 'molecule_name', 'molecule_name': 'atom_count'}, axis=1, inplace=True)
train = pd.merge(train, atom_cnt, how='left', on='molecule_name')
test = pd.merge(test, atom_cnt, how='left', on='molecule_name')
del atom_cnt
def lr(df):
df['atom_index_0l'] = df['atom_index_0'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_0', 'atom_count']]
df['atom_index_0r'] = tmp.apply(lambda row: min(row['atom_index_0'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0r'}, axis=1)
df['atom_index_1l'] = df['atom_index_1'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_1', 'atom_count']]
df['atom_index_1r'] = tmp.apply(lambda row: min(row['atom_index_1'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1r'}, axis=1)
return df
train = lr(train)
test = lr(test)
train = atom_props(train, '0')
train = atom_props(train, '0l')
train = atom_props(train, '0r')
train = atom_props(train, '1')
train = atom_props(train, '1l')
train = atom_props(train, '1r')
test = atom_props(test, '0')
test = atom_props(test, '0l')
test = atom_props(test, '0r')
test = atom_props(test, '1')
test = atom_props(test, '1l')
test = atom_props(test, '1r')
train.drop(['atom_index_x', 'atom_index_y'], axis=1, inplace=True)
test.drop(['atom_index_x', 'atom_index_y'], axis=1, inplace=True)
# https://www.kaggle.com/c/champs-scalar-coupling/discussion/96655#latest-558745
def reduce_mem_usage(df, verbose=True):
numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
start_mem = df.memory_usage().sum() / 1024**2
for col in df.columns:
col_type = df[col].dtypes
if col_type in numerics:
c_min = df[col].min()
c_max = df[col].max()
if str(col_type)[:3] == 'int':
if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
df[col] = df[col].astype(np.int8)
elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
df[col] = df[col].astype(np.int16)
elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
df[col] = df[col].astype(np.int32)
elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
df[col] = df[col].astype(np.int64)
else:
c_prec = df[col].apply(lambda x: np.finfo(x).precision).max()
if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max and c_prec == np.finfo(np.float16).precision:
df[col] = df[col].astype(np.float16)
elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max and c_prec == np.finfo(np.float32).precision:
df[col] = df[col].astype(np.float32)
else:
df[col] = df[col].astype(np.float64)
end_mem = df.memory_usage().sum() / 1024**2
if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))
return df
train = reduce_mem_usage(train)
test = reduce_mem_usage(test) | code |
16130176/cell_12 | [
"application_vnd.jupyter.stderr_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 = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv')
atomic_radius = {'H': 0.38, 'C': 0.77, 'N': 0.75, 'O': 0.73, 'F': 0.71, np.nan: 0}
atomic_number = {'H': 1, 'C': 6, 'N': 7, 'O': 8, 'F': 9, np.nan: 0}
atomic_mass = {'H': 1.0079, 'C': 12.0107, 'N': 14.0067, 'O': 15.9994, 'F': 18.9984, np.nan: 0}
vanderwaalsradius = {'H': 120, 'C': 185, 'N': 154, 'O': 140, 'F': 135, np.nan: 0}
covalenzradius = {'H': 30, 'C': 77, 'N': 70, 'O': 66, 'F': 58, np.nan: 0}
electronegativity = {'H': 2.2, 'C': 2.55, 'N': 3.04, 'O': 3.44, 'F': 3.98, np.nan: 0}
ionization_energy = {'H': 13.5984, 'C': 11.2603, 'N': 14.5341, 'O': 13.6181, 'F': 17.4228, np.nan: np.inf}
def atom_props(df, suffix):
df['atomic_radius' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_radius[x])
df['atomic_protons' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_number[x])
df['atomic_mass' + suffix] = df['atom_' + suffix].apply(lambda x: atomic_mass[x])
df['vanderwaalsradius' + suffix] = df['atom_' + suffix].apply(lambda x: vanderwaalsradius[x])
df['covalenzradius' + suffix] = df['atom_' + suffix].apply(lambda x: covalenzradius[x])
df['electronegativity' + suffix] = df['atom_' + suffix].apply(lambda x: electronegativity[x])
df['ionization_energy' + suffix] = df['atom_' + suffix].apply(lambda x: ionization_energy[x])
return df
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
train = pd.merge(train, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0'}, axis=1)
test = pd.merge(test, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1'}, axis=1)
atom_cnt = structures['molecule_name'].value_counts().reset_index(level=0)
atom_cnt.rename({'index': 'molecule_name', 'molecule_name': 'atom_count'}, axis=1, inplace=True)
train = pd.merge(train, atom_cnt, how='left', on='molecule_name')
test = pd.merge(test, atom_cnt, how='left', on='molecule_name')
del atom_cnt
def lr(df):
df['atom_index_0l'] = df['atom_index_0'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_0', 'atom_count']]
df['atom_index_0r'] = tmp.apply(lambda row: min(row['atom_index_0'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_0r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_0r'}, axis=1)
df['atom_index_1l'] = df['atom_index_1'].apply(lambda i: max(i - 1, 0))
tmp = df[['atom_index_1', 'atom_count']]
df['atom_index_1r'] = tmp.apply(lambda row: min(row['atom_index_1'] + 1, row['atom_count']), axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1l'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1l'}, axis=1)
df = pd.merge(df, structures[['molecule_name', 'atom_index', 'atom']], how='left', left_on=['molecule_name', 'atom_index_1r'], right_on=['molecule_name', 'atom_index']).rename({'atom': 'atom_1r'}, axis=1)
return df
train = lr(train)
test = lr(test)
train = atom_props(train, '0')
train = atom_props(train, '0l')
train = atom_props(train, '0r')
train = atom_props(train, '1')
train = atom_props(train, '1l')
train = atom_props(train, '1r')
test = atom_props(test, '0')
test = atom_props(test, '0l')
test = atom_props(test, '0r')
test = atom_props(test, '1')
test = atom_props(test, '1l')
test = atom_props(test, '1r') | code |
16130176/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
structures = pd.read_csv('input/structures.csv') | code |
16151986/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_selection import VarianceThreshold
from sklearn.metrics import roc_auc_score
from sklearn.mixture import GaussianMixture
import numpy as np
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-copper-turtle-magic'].astype('category')
test['wheezy-copper-turtle-magic'] = test['wheezy-copper-turtle-magic'].astype('category')
magicNum = 131073
default_cols = [c for c in train.columns if c not in ['id', 'target', 'target_pred', 'wheezy-copper-turtle-magic']]
cols = [c for c in default_cols]
sub = pd.read_csv('../input/sample_submission.csv')
sub.to_csv('submission.csv', index=False)
(train.shape, test.shape)
if sub.shape[0] == magicNum:
[].shape
preds = np.zeros(len(test))
train_err = np.zeros(512)
test_err = np.zeros(512)
for i in range(512):
X = train[train['wheezy-copper-turtle-magic'] == i].copy()
Y = X.pop('target').values
X_test = test[test['wheezy-copper-turtle-magic'] == i].copy()
idx_train = X.index
idx_test = X_test.index
X.reset_index(drop=True, inplace=True)
X = X[cols].values
X_test = X_test[cols].values
vt = VarianceThreshold(threshold=2).fit(X)
X = vt.transform(X)
X_test = vt.transform(X_test)
X_all = np.concatenate([X, X_test])
train_size = len(X)
test1_size = test[:131073][test[:131073]['wheezy-copper-turtle-magic'] == i].shape[0]
compo_cnt = 6
for ii in range(30):
gmm = GaussianMixture(n_components=compo_cnt, init_params='random', covariance_type='full', max_iter=100, tol=1e-10, reg_covar=0.0001).fit(X_all)
labels = gmm.predict(X_all)
cntStd = np.std([len(labels[labels == j]) for j in range(compo_cnt)])
if round(cntStd, 4) == 0.4714:
check_labels = labels[:train_size]
cvt_labels = np.zeros(len(labels))
for iii in range(compo_cnt):
mean_val = Y[check_labels == iii].mean()
mean_val = 1 if mean_val > 0.5 else 0
cvt_labels[labels == iii] = mean_val
train_err[i] = len(Y[Y != cvt_labels[:train_size]])
if train_err[i] >= 10 and train_err[i] <= 15:
train_err[i] = 12.5
exp_err = max(0, (25 - train_err[i]) / (train_size + test1_size))
for iii in range(compo_cnt):
mean_val = Y[check_labels == iii].mean()
mean_val = 1 - exp_err if mean_val > 0.5 else exp_err
cvt_labels[labels == iii] = mean_val
check_acc = roc_auc_score(Y, cvt_labels[:train_size])
preds[idx_test] = cvt_labels[train_size:]
break
sub['target'] = preds
sub.to_csv('submission.csv', index=False) | code |
16151986/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['wheezy-copper-turtle-magic'] = train['wheezy-copper-turtle-magic'].astype('category')
test['wheezy-copper-turtle-magic'] = test['wheezy-copper-turtle-magic'].astype('category')
magicNum = 131073
default_cols = [c for c in train.columns if c not in ['id', 'target', 'target_pred', 'wheezy-copper-turtle-magic']]
cols = [c for c in default_cols]
sub = pd.read_csv('../input/sample_submission.csv')
sub.to_csv('submission.csv', index=False)
(train.shape, test.shape) | code |
73074157/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
plt.xlabel('Population in city in 10,000s')
plt.ylabel('Profit in £10,000s')
plt.title('Relationship between city size and profit size')
plt.plot(df.iloc[:, 0], df.iloc[:, 1], 'ro', mec='k')
plt.legend(['Dataset']) | code |
73074157/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
df.info() | code |
73074157/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df | code |
73074157/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
X = df.iloc[:, :-1]
y = df.iloc[:, 1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1 / 4, random_state=42)
X_train = X_train.reset_index(drop=True)
y_train = y_train.reset_index(drop=True)
X_test = X_test.reset_index(drop=True)
y_test = y_test.reset_index(drop=True)
m, n = X_train.values.shape
o, p = X_test.values.shape
X_train = pd.concat((pd.DataFrame(np.ones((m, 1)), columns=['Bias']), X_train), axis=1)
X_test = pd.concat((pd.DataFrame(np.ones((o, 1)), columns=['Bias']), X_test), axis=1)
pop_prof_model = LinearRegression()
pop_prof_model.fit(X_train, y_train)
y_pred_train = pop_prof_model.predict(X_train)
MSE_train = pop_prof_model.LR_Cost(X_train, y_train)
print('Theta estimates are: {}'.format(pop_prof_model.theta))
print('Training dataset mean squared error: {}'.format(MSE_train)) | code |
73074157/cell_37 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
X = df.iloc[:, :-1]
y = df.iloc[:, 1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1 / 4, random_state=42)
X_train = X_train.reset_index(drop=True)
y_train = y_train.reset_index(drop=True)
X_test = X_test.reset_index(drop=True)
y_test = y_test.reset_index(drop=True)
m, n = X_train.values.shape
o, p = X_test.values.shape
X_train = pd.concat((pd.DataFrame(np.ones((m, 1)), columns=['Bias']), X_train), axis=1)
X_test = pd.concat((pd.DataFrame(np.ones((o, 1)), columns=['Bias']), X_test), axis=1)
pop_prof_model = LinearRegression()
pop_prof_model.fit(X_train, y_train)
y_pred_train = pop_prof_model.predict(X_train)
MSE_train = pop_prof_model.LR_Cost(X_train, y_train)
pop_prof_model.fit(X_test, y_test)
y_pred_test = pop_prof_model.predict(X_test)
MSE_test = pop_prof_model.LR_Cost(X_test, y_test)
plt.xlabel('Population in city in 10,000s')
plt.ylabel('Profit in £10,000s')
plt.title('Relationship between city size and profit size')
plt.plot(X_test.iloc[:, 1], y_test, 'ro', mec='k')
plt.plot(X_test.iloc[:, 1], y_pred_test, '-b', mec='k')
plt.legend(['Dataset', 'Linear Regression']) | code |
73074157/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/batchgradientdescent/ex1data1.csv', header=None)
df
df.columns = ['Population of City in 10,000s', 'Profit in £10,000s']
X = df.iloc[:, :-1]
y = df.iloc[:, 1]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1 / 4, random_state=42)
X_train = X_train.reset_index(drop=True)
y_train = y_train.reset_index(drop=True)
X_test = X_test.reset_index(drop=True)
y_test = y_test.reset_index(drop=True)
m, n = X_train.values.shape
o, p = X_test.values.shape
X_train = pd.concat((pd.DataFrame(np.ones((m, 1)), columns=['Bias']), X_train), axis=1)
X_test = pd.concat((pd.DataFrame(np.ones((o, 1)), columns=['Bias']), X_test), axis=1)
pop_prof_model = LinearRegression()
pop_prof_model.fit(X_train, y_train)
y_pred_train = pop_prof_model.predict(X_train)
MSE_train = pop_prof_model.LR_Cost(X_train, y_train)
pop_prof_model.fit(X_test, y_test)
y_pred_test = pop_prof_model.predict(X_test)
MSE_test = pop_prof_model.LR_Cost(X_test, y_test)
print('Theta estimates are: {}'.format(pop_prof_model.theta))
print('Test dataset mean squared error: {}'.format(MSE_test)) | code |
17120135/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
df_BTM =df_main.loc[df_main['location']=='BTM']
df_BTM_REST= df_BTM['rest_type'].value_counts()
fig = plt.figure(figsize=(20,10))
ax1 = fig.add_subplot(121)
sns.barplot(x=df_BTM_REST, y= df_BTM_REST.index,ax=ax1)
plt.title('Count of restaurant types in BTM')
plt.xlabel('Count')
plt.ylabel('Restaurant Name')
df_BTM_REST1 = df_BTM_REST[:10]
labels = df_BTM_REST1.index
explode = (0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0)
df_RATE_BTM = df_BTM[['rate', 'rest_type', 'online_order', 'votes', 'book_table', 'approx_cost(for two people)', 'listed_in(type)', 'listed_in(city)']].dropna()
df_RATE_BTM['rate'] = df_RATE_BTM['rate'].apply(lambda x: float(x.split('/')[0]) if len(x) > 3 else 0)
df_RATE_BTM['approx_cost(for two people)'] = df_RATE_BTM['approx_cost(for two people)'].apply(lambda x: int(x.replace(',', '')))
df_rating = df_BTM['rate'].dropna().apply(lambda x: float(x.split('/')[0]) if len(x) > 3 else np.nan).dropna()
f, axes = plt.subplots(1, 2, figsize=(20, 10), sharex=True)
sns.despine(left=True)
sns.distplot(df_rating, bins=20, ax=axes[0]).set_title('Rating distribution in BTM Region')
plt.xlabel('Rating')
df_grp = df_RATE_BTM.groupby(by='rest_type').agg('mean').sort_values(by='votes', ascending=False)
sns.distplot(df_grp['rate'], bins=20, ax=axes[1]).set_title('Average Rating distribution in BTM Region') | code |
17120135/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df_main = pd.read_csv('../input/zomato.csv')
df_loc = df_main['location'].value_counts()[:20]
plt.figure(figsize=(20, 10))
sns.barplot(x=df_loc, y=df_loc.index)
plt.title('Top 20 locations with highest number of Restaurants.')
plt.xlabel('Count')
plt.ylabel('Restaurant Name') | code |
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