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17139154/cell_31 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1)
plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title('Correlation Matrix')
plt.show()
df = df.drop(columns=['cod_municipio_tse'])
x = sns.PairGrid(df)
x.map(plt.scatter)
uf = pd.DataFrame(df['uf'].value_counts())
eleitores = df[['uf', 'total_eleitores']].sort_values(by='uf')
eleitores_grpd_by_uf = eleitores.groupby(['uf']).sum()
norte = ['AM', 'RR', 'AP', 'PA', 'TO', 'RO', 'AC']
centroeste = ['MT', 'MS', 'GO']
sudeste = ['SP', 'ES', 'MG', 'RJ']
sul = ['PR', 'RS', 'SC']
nordeste = ['MA', 'PI', 'CE', 'RN', 'PE', 'PB', 'SE', 'AL', 'BA']
df_region = eleitores
df_region['regiao'] = ''
for i, r in df_region.iterrows():
if r['uf'] in norte:
df_region.at[i, 'regiao'] = 'Norte'
elif r['uf'] in centroeste:
df_region.at[i, 'regiao'] = 'Centro-Oeste'
elif r['uf'] in sudeste:
df_region.at[i, 'regiao'] = 'Sudeste'
elif r['uf'] in sul:
df_region.at[i, 'regiao'] = 'Sul'
else:
df_region.at[i, 'regiao'] = 'Nordeste'
df_ufs = pd.DataFrame(norte + centroeste + sudeste + sul + nordeste)
reg = pd.DataFrame(df_region['regiao'].value_counts())
elec = pd.DataFrame(df_region.drop(columns=['uf']).groupby(['regiao']).sum())
plt.figure(figsize=(10, 15))
sns.violinplot(y='total_eleitores', x='regiao', data=df_region) | code |
17139154/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1)
plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title('Correlation Matrix')
plt.show()
df = df.drop(columns=['cod_municipio_tse'])
df['uf'].value_counts().count() | code |
17139154/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape
corr = df.corr()
plt.figure(num=None, dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1)
plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title('Correlation Matrix')
plt.show()
df = df.drop(columns=['cod_municipio_tse'])
x = sns.PairGrid(df)
x.map(plt.scatter)
uf = pd.DataFrame(df['uf'].value_counts())
eleitores = df[['uf', 'total_eleitores']].sort_values(by='uf')
eleitores_grpd_by_uf = eleitores.groupby(['uf']).sum()
plt.figure(figsize=(15, 5))
plt.title('Total de eleitores em cada UF')
sns.barplot(x=eleitores_grpd_by_uf.index, y=eleitores_grpd_by_uf.total_eleitores) | code |
17139154/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/BR_eleitorado_2016_municipio.csv', delimiter=';')
df.shape | code |
129016252/cell_13 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.axes)
print('-' * 30)
print(p.axis(axis=Axis.X))
print('-' * 30)
print(p.axis(axis=Axis.Y))
print('-' * 30)
print(p.ticks(axis=Axis.X))
print('-' * 30)
print(p.ticks(axis=Axis.X, filter='id'))
print('-' * 30)
print(p.ticks(axis=Axis.X, filter='tick_pt'))
print('-' * 30)
print(p.tick_type(axis=Axis.X))
print('-' * 30)
print(p.values_type(axis=Axis.X)) | code |
129016252/cell_9 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.chart_type) | code |
129016252/cell_11 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.text()) | code |
129016252/cell_7 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.name)
print(p.json_path)
print(p.image_path) | code |
129016252/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image, ImageDraw
from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
from typing import Dict
import matplotlib.pyplot as plt
import random
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
from typing import Dict
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw
def draw_rect_angle(img: Image.Image, rectangle_coord: Dict[str, int]):
x0 = rectangle_coord['x0']
y0 = rectangle_coord['y0']
h = rectangle_coord['height']
w = rectangle_coord['width']
draw = ImageDraw.Draw(img)
draw.rectangle([(x0, y0), (x0 + w, y0 + h)], outline='blue', width=0)
def draw_rect_angle_rotate(img: Image.Image, rectangle_coord: Dict[str, int], annotation_type: str):
x0 = rectangle_coord['x0']
x1 = rectangle_coord['x1']
x2 = rectangle_coord['x2']
x3 = rectangle_coord['x3']
y0 = rectangle_coord['y0']
y1 = rectangle_coord['y1']
y2 = rectangle_coord['y2']
y3 = rectangle_coord['y3']
draw = ImageDraw.Draw(img)
color = {'tick_label': (255, 0, 0), 'chart_title': (0, 192, 192), 'axis_title': (255, 255, 0)}[annotation_type]
draw.line([x0, y0, x1, y1], fill=color, width=2)
draw.line([x1, y1, x2, y2], fill=color, width=2)
draw.line([x2, y2, x3, y3], fill=color, width=2)
draw.line([x3, y3, x0, y0], fill=color, width=2)
def draw_point(img: Image.Image, coord: Dict[str, int]):
draw = ImageDraw.Draw(img)
draw.ellipse([(coord['x'] - 1, coord['y'] - 1), (coord['x'] + 1, coord['y'] + 1)], fill='lime', outline='lime', width=10)
def visualization_show(annotation_parser: AnnotationParser, index: int):
ap = annotation_parser.get_annotation(index=index)
img = Image.open(ap.image_path)
polygon = ap.text(filter='polygon')
role = ap.text(filter='role')
ticks_x = ap.ticks(axis=Axis.X, filter='tick_pt')
ticks_y = ap.ticks(axis=Axis.Y, filter='tick_pt')
plt.axis('off')
import random
for i in range(5):
random_index = random.randint(0, len(annotation_parser))
visualization_show(annotation_parser, random_index) | code |
129016252/cell_8 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.source) | code |
129016252/cell_3 | [
"text_plain_output_1.png"
] | # api install
!pip install benetech-annotation-parser | code |
129016252/cell_14 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.data_series())
print('-' * 30)
print(p.data_series(filter='x'))
print('-' * 30)
print(p.data_series(filter='y')) | code |
129016252/cell_10 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.plot_bb) | code |
129016252/cell_12 | [
"text_plain_output_1.png"
] | from benetech_annotation_parser.annotation_api import AnnotationParser, Axis
train_dataset_path = '/kaggle/input/benetech-making-graphs-accessible/train'
annotation_parser = AnnotationParser(train_dataset_path)
p = annotation_parser.get_annotation(0)
print(p.text(filter='id'))
print('-' * 30)
print(p.text(filter='polygon'))
print('-' * 30)
print(p.text(filter='text'))
print('-' * 30)
print(p.text(filter='role')) | code |
328841/cell_4 | [
"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/act_train.csv', parse_dates=['date'])
test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])
ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])
df_train = pd.merge(train, ppl, on='people_id')
df_test = pd.merge(test, ppl, on='people_id')
del train, test, ppl
for d in ['date_x', 'date_y']:
print('Start of ' + d + ': ' + str(df_train[d].min().date()))
print(' End of ' + d + ': ' + str(df_train[d].max().date()))
print('Range of ' + d + ': ' + str(df_train[d].max() - df_train[d].min()) + '\n') | code |
106208028/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)
scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])
print(X.shape)
print(y.shape) | code |
106208028/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x='wifi', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106208028/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.head() | code |
106208028/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)
scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])
kf = KFold(n_splits=5)
kf.get_n_splits(X) | code |
106208028/cell_20 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test)
confusion_matrix(y_test, clf.predict(X_test)) | code |
106208028/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.info() | code |
106208028/cell_2 | [
"image_output_1.png"
] | !pip install pydotplus | code |
106208028/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(), cmap='BrBG', annot=True, linewidths=2.0) | code |
106208028/cell_19 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train)
clf.score(X_test, y_test) | code |
106208028/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106208028/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any() | code |
106208028/cell_18 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
clf.score(X_train, y_train) | code |
106208028/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)
scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])
kf = KFold(n_splits=5)
kf.get_n_splits(X)
training_scores_log = []
testing_scores_log = []
for fold, (train_index, test_index) in enumerate(kf.split(X)):
X_train = X[train_index]
y_train = y[train_index]
X_test = X[test_index]
y_test = y[test_index]
clf_log = LogisticRegression(random_state=0).fit(X_train, y_train)
training_scores_log.append(clf_log.score(X_train, y_train))
testing_scores_log.append(clf_log.score(X_test, y_test))
training_scores_svm_lin = []
testing_scores_svm_lin = []
for fold, (train_index, test_index) in enumerate(kf.split(X)):
X_train = X[train_index]
y_train = y[train_index]
X_test = X[test_index]
y_test = y[test_index]
clf_svm_lin = SVC(kernel='linear').fit(X_train, y_train)
print(f'Fold {fold + 1} -> The score of the training data set is: ', clf_svm_lin.score(X_train, y_train))
print(f'Fold {fold + 1} -> The score of the testing (out of fold) data set is: ', clf_svm_lin.score(X_test, y_test))
training_scores_svm_lin.append(clf_svm_lin.score(X_train, y_train))
testing_scores_svm_lin.append(clf_svm_lin.score(X_test, y_test))
print('\n')
print(f'The average training set accuracy is: {sum(training_scores_svm_lin) / len(training_scores_svm_lin)}')
print(f'The average testing set accuracy is: {sum(testing_scores_svm_lin) / len(testing_scores_svm_lin)}') | code |
106208028/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x='blue', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106208028/cell_15 | [
"text_plain_output_1.png"
] | X_train | code |
106208028/cell_16 | [
"text_plain_output_1.png"
] | X_test | code |
106208028/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold, train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="n_cores",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
plt.figure(figsize=(15, 12))
g = sns.heatmap(train.corr(),cmap="BrBG",annot=True, linewidths = 2.0)
scaler = StandardScaler()
X = scaler.fit_transform(train.drop(['price_range'], axis=1))
y = np.ravel(train[['price_range']])
kf = KFold(n_splits=5)
kf.get_n_splits(X)
training_scores_log = []
testing_scores_log = []
for fold, (train_index, test_index) in enumerate(kf.split(X)):
X_train = X[train_index]
y_train = y[train_index]
X_test = X[test_index]
y_test = y[test_index]
clf_log = LogisticRegression(random_state=0).fit(X_train, y_train)
print(f'Fold {fold + 1} -> The score of the training data set is: ', clf_log.score(X_train, y_train))
print(f'Fold {fold + 1} -> The score of the testing (out of fold) data set is: ', clf_log.score(X_test, y_test))
training_scores_log.append(clf_log.score(X_train, y_train))
testing_scores_log.append(clf_log.score(X_test, y_test))
print('\n')
print(f'The average training set accuracy is: {sum(training_scores_log) / len(training_scores_log)}')
print(f'The average testing set accuracy is: {sum(testing_scores_log) / len(testing_scores_log)}') | code |
106208028/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.isna().any()
g = sns.catplot(x="blue",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x="wifi",y="price_range",data=train, kind = 'bar', height = 6,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("price_range")
g = sns.catplot(x='n_cores', y='price_range', data=train, kind='bar', height=6, palette='muted')
g.despine(left=True)
g = g.set_ylabels('price_range') | code |
106208028/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
train.describe() | code |
72068164/cell_9 | [
"image_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
daily_activities.info()
daily_calories.info()
daily_intensities.info()
daily_steps.info() | code |
72068164/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
sleep_day.info()
weight_index.info() | code |
72068164/cell_28 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
len(weight_index.Id.unique())
len(daily_activities.Id.unique())
len(sleep_day.Id.unique())
sleep_day.columns = ['Id', 'Date', 'TotalSleepRecords', 'TotalMinutesAsleep', 'TotalTimeInBed']
daily_activities.columns = ['Id', 'Date', 'TotalSteps', 'TotalDistance', 'TrackerDistance', 'LoggedActivitiesDistance', 'VeryActiveDistance', 'ModeratelyActiveDistance', 'LightActiveDistance', 'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']
sleep_day.Date = pd.to_datetime(sleep_day['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
daily_activities.Date = pd.to_datetime(daily_activities['Date'], format='%m/%d/%Y').dt.strftime('%m/%d/%Y')
weight_index.Date = pd.to_datetime(weight_index['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
#Change date into day of week
index_day=[]
week_day=[]
for i in range(len(daily_activities.Id)):
day=dt.datetime.strptime(daily_activities.Date[i], "%m/%d/%Y")
week_day.append(day.strftime("%A"))
index_day.append(day.weekday())
i=i+1
daily_activities["DayofWeek"]=week_day
daily_activities["IndexofWeek"]=index_day
gb_daily_activities=daily_activities.groupby(by=['DayofWeek']).mean().reset_index()
gb_daily_activities.sort_values("IndexofWeek")
sns.set_theme(style="darkgrid")
plt.figure(figsize=(10,8))
ax=sns.barplot(x="DayofWeek", y="TotalSteps",data=gb_daily_activities.sort_values("IndexofWeek"), palette="Accent_r")
ax.set_ylabel("Average Steps",fontsize=20)
ax.set_xlabel("Day of Week ",fontsize=20)
ax.set_title("Average Steps During Week ",fontsize=25)
plt.show()
hour = []
for i in range(len(hourly_steps.Id)):
day = dt.datetime.strptime(hourly_steps.ActivityHour[i], '%m/%d/%Y %I:%M:%S %p')
hour.append(day.strftime('%H'))
i = i + 1
hourly_steps['Hour'] = hour
gb_hourly_steps = hourly_steps.groupby('Hour').mean().reset_index()
sns.set_theme(style='darkgrid')
plt.figure(figsize=(10, 8))
hourly_chart = sns.barplot(data=gb_hourly_steps, x='Hour', y='StepTotal')
hourly_chart.set_ylabel('Average Steps', fontsize=20)
hourly_chart.set_xlabel('Hour', fontsize=20)
hourly_chart.set_title('Hourly distribution of user steps', fontsize=25)
plt.show() | code |
72068164/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
len(weight_index.Id.unique()) | code |
72068164/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
len(daily_activities.Id.unique()) | code |
72068164/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
len(sleep_day.Id.unique()) | code |
72068164/cell_31 | [
"image_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
len(weight_index.Id.unique())
len(daily_activities.Id.unique())
len(sleep_day.Id.unique())
sleep_day.columns = ['Id', 'Date', 'TotalSleepRecords', 'TotalMinutesAsleep', 'TotalTimeInBed']
daily_activities.columns = ['Id', 'Date', 'TotalSteps', 'TotalDistance', 'TrackerDistance', 'LoggedActivitiesDistance', 'VeryActiveDistance', 'ModeratelyActiveDistance', 'LightActiveDistance', 'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']
sleep_day.Date = pd.to_datetime(sleep_day['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
daily_activities.Date = pd.to_datetime(daily_activities['Date'], format='%m/%d/%Y').dt.strftime('%m/%d/%Y')
weight_index.Date = pd.to_datetime(weight_index['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
#Change date into day of week
index_day=[]
week_day=[]
for i in range(len(daily_activities.Id)):
day=dt.datetime.strptime(daily_activities.Date[i], "%m/%d/%Y")
week_day.append(day.strftime("%A"))
index_day.append(day.weekday())
i=i+1
daily_activities["DayofWeek"]=week_day
daily_activities["IndexofWeek"]=index_day
gb_daily_activities=daily_activities.groupby(by=['DayofWeek']).mean().reset_index()
gb_daily_activities.sort_values("IndexofWeek")
sns.set_theme(style="darkgrid")
plt.figure(figsize=(10,8))
ax=sns.barplot(x="DayofWeek", y="TotalSteps",data=gb_daily_activities.sort_values("IndexofWeek"), palette="Accent_r")
ax.set_ylabel("Average Steps",fontsize=20)
ax.set_xlabel("Day of Week ",fontsize=20)
ax.set_title("Average Steps During Week ",fontsize=25)
plt.show()
hour=[]
for i in range(len(hourly_steps.Id)):
day=dt.datetime.strptime(hourly_steps.ActivityHour[i], "%m/%d/%Y %I:%M:%S %p")
hour.append(day.strftime("%H"))
i=i+1
hourly_steps["Hour"]=hour
gb_hourly_steps=hourly_steps.groupby('Hour').mean().reset_index()
sns.set_theme(style="darkgrid")
plt.figure(figsize=(10,8))
hourly_chart=sns.barplot(data=gb_hourly_steps,x="Hour",y="StepTotal")
hourly_chart.set_ylabel("Average Steps",fontsize=20)
hourly_chart.set_xlabel("Hour",fontsize=20)
hourly_chart.set_title("Hourly distribution of user steps",fontsize=25)
plt.show()
sleep_day['TimeTakeToSleep'] = sleep_day['TotalTimeInBed'] - sleep_day['TotalMinutesAsleep']
hourly_intensities.columns = ['Id', 'Date', 'TotalIntensity', 'AverageIntensity']
hourly_intensities.Date = pd.to_datetime(hourly_intensities['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
gb_hourly_intensities = hourly_intensities.groupby(['Date', 'Id']).sum().reset_index()
sleep_and_intensities = pd.merge(sleep_day, gb_hourly_intensities, on=['Date', 'Id'], how='inner')
f, axes = plt.subplots(1, 3, figsize=(12, 6))
k1 = sns.regplot(data=sleep_and_intensities, x='TimeTakeToSleep', y='TotalIntensity', ax=axes[1])
k2 = sns.regplot(data=sleep_and_intensities, x='TotalMinutesAsleep', y='TotalIntensity', ax=axes[0])
k2 = sns.regplot(data=sleep_and_intensities, x='TotalTimeInBed', y='TotalIntensity', ax=axes[2]) | code |
72068164/cell_24 | [
"text_html_output_1.png"
] | import datetime as dt
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
len(weight_index.Id.unique())
len(daily_activities.Id.unique())
len(sleep_day.Id.unique())
sleep_day.columns = ['Id', 'Date', 'TotalSleepRecords', 'TotalMinutesAsleep', 'TotalTimeInBed']
daily_activities.columns = ['Id', 'Date', 'TotalSteps', 'TotalDistance', 'TrackerDistance', 'LoggedActivitiesDistance', 'VeryActiveDistance', 'ModeratelyActiveDistance', 'LightActiveDistance', 'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']
sleep_day.Date = pd.to_datetime(sleep_day['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
daily_activities.Date = pd.to_datetime(daily_activities['Date'], format='%m/%d/%Y').dt.strftime('%m/%d/%Y')
weight_index.Date = pd.to_datetime(weight_index['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
index_day = []
week_day = []
for i in range(len(daily_activities.Id)):
day = dt.datetime.strptime(daily_activities.Date[i], '%m/%d/%Y')
week_day.append(day.strftime('%A'))
index_day.append(day.weekday())
i = i + 1
daily_activities['DayofWeek'] = week_day
daily_activities['IndexofWeek'] = index_day
gb_daily_activities = daily_activities.groupby(by=['DayofWeek']).mean().reset_index()
gb_daily_activities.sort_values('IndexofWeek')
sns.set_theme(style='darkgrid')
plt.figure(figsize=(10, 8))
ax = sns.barplot(x='DayofWeek', y='TotalSteps', data=gb_daily_activities.sort_values('IndexofWeek'), palette='Accent_r')
ax.set_ylabel('Average Steps', fontsize=20)
ax.set_xlabel('Day of Week ', fontsize=20)
ax.set_title('Average Steps During Week ', fontsize=25)
plt.show() | code |
72068164/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
weight_index.head() | code |
72068164/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
len(weight_index.Id.unique())
len(daily_activities.Id.unique())
len(sleep_day.Id.unique())
sleep_day.columns = ['Id', 'Date', 'TotalSleepRecords', 'TotalMinutesAsleep', 'TotalTimeInBed']
daily_activities.columns = ['Id', 'Date', 'TotalSteps', 'TotalDistance', 'TrackerDistance', 'LoggedActivitiesDistance', 'VeryActiveDistance', 'ModeratelyActiveDistance', 'LightActiveDistance', 'SedentaryActiveDistance', 'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes', 'Calories']
sleep_day.Date = pd.to_datetime(sleep_day['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
daily_activities.Date = pd.to_datetime(daily_activities['Date'], format='%m/%d/%Y').dt.strftime('%m/%d/%Y')
weight_index.Date = pd.to_datetime(weight_index['Date'], format='%m/%d/%Y %I:%M:%S %p').dt.strftime('%m/%d/%Y')
daily_activities.describe() | code |
72068164/cell_27 | [
"image_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
hourly_steps.head() | code |
72068164/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
daily_activities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyActivity_merged.csv')
daily_calories = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyCalories_merged.csv')
daily_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailyIntensities_merged.csv')
daily_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/dailySteps_merged.csv')
hourly_steps = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlySteps_merged.csv')
hourly_intensities = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/hourlyIntensities_merged.csv')
sleep_day = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/sleepDay_merged.csv')
weight_index = pd.read_csv('../input/fitbit/Fitabase Data 4.12.16-5.12.16/weightLogInfo_merged.csv')
sleep_day.head() | code |
89141749/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89141749/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
prices = pd.read_csv('../input/avocado/avocado.csv', index_col=0)
prices_2018 = prices.query("Date >= '2018-01-01' & Date <= '2018-12-31'")
prices_2018
grouped_2018 = prices_2018.groupby('region')['AveragePrice'].mean()
grouped_2018 = grouped_2018.reset_index()
grouped_2018
plt.figure(figsize=(14, 12))
sns.barplot(x=grouped_2018.AveragePrice, y=grouped_2018['region'])
plt.xlabel('')
plt.title('Average Price for Avocado, by Regions') | code |
89141749/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
prices = pd.read_csv('../input/avocado/avocado.csv', index_col=0)
prices_2018 = prices.query("Date >= '2018-01-01' & Date <= '2018-12-31'")
prices_2018 | code |
89141749/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
prices = pd.read_csv('../input/avocado/avocado.csv', index_col=0)
prices_2018 = prices.query("Date >= '2018-01-01' & Date <= '2018-12-31'")
prices_2018
grouped_2018 = prices_2018.groupby('region')['AveragePrice'].mean()
grouped_2018 = grouped_2018.reset_index()
grouped_2018 | code |
17133772/cell_30 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
import lightgbm as lgb
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int))
ks = ks.assign(hour=ks.launched.dt.hour, day=ks.launched.dt.day, month=ks.launched.dt.month, year=ks.launched.dt.year)
cat_features = ['category', 'currency', 'country']
encoder = LabelEncoder()
encoded = ks[cat_features].apply(encoder.fit_transform)
data = ks[['goal', 'hour', 'day', 'month', 'year', 'outcome']].join(encoded)
valid_fraction = 0.1
valid_size = int(len(data) * valid_fraction)
train = data[:-2 * valid_size]
valid = data[-2 * valid_size:-valid_size]
test = data[-valid_size:]
import lightgbm as lgb
feature_cols = train.columns.drop('outcome')
dtrain = lgb.Dataset(train[feature_cols], label=train['outcome'])
dvalid = lgb.Dataset(valid[feature_cols], label=valid['outcome'])
param = {'num_leaves': 64, 'objective': 'binary'}
param['metric'] = 'auc'
num_round = 1000
bst = lgb.train(param, dtrain, num_round, valid_sets=[dvalid], early_stopping_rounds=10, verbose_eval=False)
ypred = bst.predict(test[feature_cols])
score = metrics.roc_auc_score(test['outcome'], ypred)
print(f'Test AUC score: {score}') | code |
17133772/cell_20 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int))
ks = ks.assign(hour=ks.launched.dt.hour, day=ks.launched.dt.day, month=ks.launched.dt.month, year=ks.launched.dt.year)
cat_features = ['category', 'currency', 'country']
encoder = LabelEncoder()
encoded = ks[cat_features].apply(encoder.fit_transform)
data = ks[['goal', 'hour', 'day', 'month', 'year', 'outcome']].join(encoded)
data.head() | code |
17133772/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
ks.head(10) | code |
17133772/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state) | code |
17133772/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int))
ks = ks.assign(hour=ks.launched.dt.hour, day=ks.launched.dt.day, month=ks.launched.dt.month, year=ks.launched.dt.year)
cat_features = ['category', 'currency', 'country']
encoder = LabelEncoder()
encoded = ks[cat_features].apply(encoder.fit_transform)
encoded.head(10) | code |
17133772/cell_24 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int))
ks = ks.assign(hour=ks.launched.dt.hour, day=ks.launched.dt.day, month=ks.launched.dt.month, year=ks.launched.dt.year)
cat_features = ['category', 'currency', 'country']
encoder = LabelEncoder()
encoded = ks[cat_features].apply(encoder.fit_transform)
data = ks[['goal', 'hour', 'day', 'month', 'year', 'outcome']].join(encoded)
valid_fraction = 0.1
valid_size = int(len(data) * valid_fraction)
train = data[:-2 * valid_size]
valid = data[-2 * valid_size:-valid_size]
test = data[-valid_size:]
for each in [train, valid, test]:
print(f'Outcome fraction = {each.outcome.mean():.4f}') | code |
17133772/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count()
ks = ks.query('state != "live"')
ks = ks.assign(outcome=(ks['state'] == 'successful').astype(int))
ks = ks.assign(hour=ks.launched.dt.hour, day=ks.launched.dt.day, month=ks.launched.dt.month, year=ks.launched.dt.year)
ks.head() | code |
17133772/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv', parse_dates=['deadline', 'launched'])
pd.unique(ks.state)
ks.groupby('state')['ID'].count() | code |
89141713/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.head() | code |
89141713/cell_25 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.isnull().sum()
sns.set_style('whitegrid')
fig = plt.figure(figsize = (12, 8))
# x distribution
ax1 = fig.add_subplot(3, 2, 1)
sns.countplot(x = 'x', data = train, palette="Set2")
plt.xticks()
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.set_xlabel("x value", fontsize=14, labelpad=10)
ax1.set_ylabel("Count", fontsize=14, labelpad=10)
ax1.set_title('Distribution of x - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax2 = fig.add_subplot(3, 2, 2)
sns.countplot(x = 'x', data = test, palette="Set2")
plt.xticks()
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.set_xlabel("x value", fontsize=14, labelpad=10)
ax2.set_ylabel("Count", fontsize=14, labelpad=10)
ax2.set_title('Distribution of x - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
# y distribution
ax3 = fig.add_subplot(3, 2, 3)
sns.countplot(x = 'y', data = train, palette="Set2")
plt.xticks()
ax3.spines['right'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.set_xlabel("y value", fontsize=14, labelpad=10)
ax3.set_ylabel("Count", fontsize=14, labelpad=10)
ax3.set_title('Distribution of y - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax4 = fig.add_subplot(3, 2, 4)
sns.countplot(x = 'y', data = test, palette="Set2")
plt.xticks()
ax4.spines['right'].set_visible(False)
ax4.spines['top'].set_visible(False)
ax4.set_xlabel("y value", fontsize=14, labelpad=10)
ax4.set_ylabel("Count", fontsize=14, labelpad=10)
ax4.set_title('Distribution of y - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
# direction distribution
ax5 = fig.add_subplot(3, 2, 5)
sns.countplot(x = 'direction', data = train, palette="Set2")
plt.xticks()
ax5.spines['right'].set_visible(False)
ax5.spines['top'].set_visible(False)
ax5.set_xlabel("direction", fontsize=14, labelpad=10)
ax5.set_ylabel("Count", fontsize=14, labelpad=10)
ax5.set_title('Distribution of direction - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax6 = fig.add_subplot(3, 2, 6)
sns.countplot(x = 'direction', data = test, palette="Set2")
plt.xticks()
ax6.spines['right'].set_visible(False)
ax6.spines['top'].set_visible(False)
ax6.set_xlabel("direction", fontsize=14, labelpad=10)
ax6.set_ylabel("Count", fontsize=14, labelpad=10)
ax6.set_title('Distribution of direction - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
fig.tight_layout()
categorical_columns = train[['x', 'y', 'direction']].columns.to_numpy()
fig = plt.figure(figsize=(10, 9))
rows = 3
cols = 1
for idx, categorical_column in enumerate(categorical_columns):
ax = fig.add_subplot(rows, cols, idx + 1)
sns.barplot(x=categorical_column, y='congestion', data=train.groupby(categorical_column).mean('congestion')['congestion'].reset_index(), palette='Set2')
ax.xaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.yaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylabel('congestion')
ax.set_xlabel(categorical_column + ' value')
ax.bar_label(ax.containers[0])
ax.set_title('Average congestion by ' + categorical_column, loc='center', fontsize=14, fontweight='bold', pad=20)
ax.legend()
fig.tight_layout()
fig.show() | code |
89141713/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.isnull().sum() | code |
89141713/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89141713/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.isnull().sum()
sns.set_style('whitegrid')
fig = plt.figure(figsize = (12, 8))
# x distribution
ax1 = fig.add_subplot(3, 2, 1)
sns.countplot(x = 'x', data = train, palette="Set2")
plt.xticks()
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.set_xlabel("x value", fontsize=14, labelpad=10)
ax1.set_ylabel("Count", fontsize=14, labelpad=10)
ax1.set_title('Distribution of x - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax2 = fig.add_subplot(3, 2, 2)
sns.countplot(x = 'x', data = test, palette="Set2")
plt.xticks()
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.set_xlabel("x value", fontsize=14, labelpad=10)
ax2.set_ylabel("Count", fontsize=14, labelpad=10)
ax2.set_title('Distribution of x - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
# y distribution
ax3 = fig.add_subplot(3, 2, 3)
sns.countplot(x = 'y', data = train, palette="Set2")
plt.xticks()
ax3.spines['right'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.set_xlabel("y value", fontsize=14, labelpad=10)
ax3.set_ylabel("Count", fontsize=14, labelpad=10)
ax3.set_title('Distribution of y - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax4 = fig.add_subplot(3, 2, 4)
sns.countplot(x = 'y', data = test, palette="Set2")
plt.xticks()
ax4.spines['right'].set_visible(False)
ax4.spines['top'].set_visible(False)
ax4.set_xlabel("y value", fontsize=14, labelpad=10)
ax4.set_ylabel("Count", fontsize=14, labelpad=10)
ax4.set_title('Distribution of y - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
# direction distribution
ax5 = fig.add_subplot(3, 2, 5)
sns.countplot(x = 'direction', data = train, palette="Set2")
plt.xticks()
ax5.spines['right'].set_visible(False)
ax5.spines['top'].set_visible(False)
ax5.set_xlabel("direction", fontsize=14, labelpad=10)
ax5.set_ylabel("Count", fontsize=14, labelpad=10)
ax5.set_title('Distribution of direction - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax6 = fig.add_subplot(3, 2, 6)
sns.countplot(x = 'direction', data = test, palette="Set2")
plt.xticks()
ax6.spines['right'].set_visible(False)
ax6.spines['top'].set_visible(False)
ax6.set_xlabel("direction", fontsize=14, labelpad=10)
ax6.set_ylabel("Count", fontsize=14, labelpad=10)
ax6.set_title('Distribution of direction - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
fig.tight_layout()
categorical_columns = train[['x', 'y', 'direction']].columns.to_numpy()
fig = plt.figure(figsize = (10, 9))
rows = 3
cols = 1
for idx, categorical_column in enumerate(categorical_columns):
ax = fig.add_subplot(rows, cols, idx + 1)
sns.barplot(x = categorical_column, y = 'congestion', data = train.groupby(categorical_column).mean('congestion')['congestion'].reset_index(), palette = 'Set2')
ax.xaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.yaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylabel('congestion')
ax.set_xlabel(categorical_column + " value")
ax.bar_label(ax.containers[0])
ax.set_title('Average congestion by ' + categorical_column, loc = 'center', fontsize = 14, fontweight = 'bold', pad = 20)
ax.legend()
fig.tight_layout()
fig.show()
train.dtypes
fig = plt.figure(figsize=(12, 3))
ax = fig.add_subplot(1, 1, 1)
sns.lineplot(x=train['time'].dt.date, y='congestion', data=train)
ax.xaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.yaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylabel('congestion')
ax.set_xlabel('date')
ax.set_title('Average congestion by date', loc='center', fontsize=14, fontweight='bold', pad=20)
fig.tight_layout()
fig.show() | code |
89141713/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
test.head() | code |
89141713/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
print(f'\x1b[92mNumber of rows in the table: {test.shape[0]}')
print(f'\x1b[94mNumber of columns in the table: {test.shape[1]}')
print(f'\x1b[91mNumber of observations in the table: {test.count().sum()}')
print(f'\x1b[91mNumber of missing values in the table: {sum(test.isnull().sum())}')
print(f'\x1b[91mNumber of duplicated records: {test.duplicated().sum()}')
print()
print(f'\x1b[95mData types')
print(f'\x1b[90m{test.dtypes}')
print()
print(f'\x1b[95mData type counts')
print(f'\x1b[90m{test.dtypes.value_counts()}')
print()
print(f'\x1b[95mUnique value in each column')
print(f'\x1b[90m{test.nunique()}') | code |
89141713/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.isnull().sum()
print('Test set rows / Train set rows = ' + str(round(test.count()[0] / train.count()[0] * 100, 2))) | code |
89141713/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.isnull().sum()
sns.set_style('whitegrid')
fig = plt.figure(figsize=(12, 8))
ax1 = fig.add_subplot(3, 2, 1)
sns.countplot(x='x', data=train, palette='Set2')
plt.xticks()
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.set_xlabel('x value', fontsize=14, labelpad=10)
ax1.set_ylabel('Count', fontsize=14, labelpad=10)
ax1.set_title('Distribution of x - Train Set', loc='center', fontsize=14, fontweight='bold')
ax2 = fig.add_subplot(3, 2, 2)
sns.countplot(x='x', data=test, palette='Set2')
plt.xticks()
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.set_xlabel('x value', fontsize=14, labelpad=10)
ax2.set_ylabel('Count', fontsize=14, labelpad=10)
ax2.set_title('Distribution of x - Test Set', loc='center', fontsize=14, fontweight='bold')
ax3 = fig.add_subplot(3, 2, 3)
sns.countplot(x='y', data=train, palette='Set2')
plt.xticks()
ax3.spines['right'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.set_xlabel('y value', fontsize=14, labelpad=10)
ax3.set_ylabel('Count', fontsize=14, labelpad=10)
ax3.set_title('Distribution of y - Train Set', loc='center', fontsize=14, fontweight='bold')
ax4 = fig.add_subplot(3, 2, 4)
sns.countplot(x='y', data=test, palette='Set2')
plt.xticks()
ax4.spines['right'].set_visible(False)
ax4.spines['top'].set_visible(False)
ax4.set_xlabel('y value', fontsize=14, labelpad=10)
ax4.set_ylabel('Count', fontsize=14, labelpad=10)
ax4.set_title('Distribution of y - Test Set', loc='center', fontsize=14, fontweight='bold')
ax5 = fig.add_subplot(3, 2, 5)
sns.countplot(x='direction', data=train, palette='Set2')
plt.xticks()
ax5.spines['right'].set_visible(False)
ax5.spines['top'].set_visible(False)
ax5.set_xlabel('direction', fontsize=14, labelpad=10)
ax5.set_ylabel('Count', fontsize=14, labelpad=10)
ax5.set_title('Distribution of direction - Train Set', loc='center', fontsize=14, fontweight='bold')
ax6 = fig.add_subplot(3, 2, 6)
sns.countplot(x='direction', data=test, palette='Set2')
plt.xticks()
ax6.spines['right'].set_visible(False)
ax6.spines['top'].set_visible(False)
ax6.set_xlabel('direction', fontsize=14, labelpad=10)
ax6.set_ylabel('Count', fontsize=14, labelpad=10)
ax6.set_title('Distribution of direction - Test Set', loc='center', fontsize=14, fontweight='bold')
fig.tight_layout() | code |
89141713/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.isnull().sum()
sns.set_style('whitegrid')
fig = plt.figure(figsize = (12, 8))
# x distribution
ax1 = fig.add_subplot(3, 2, 1)
sns.countplot(x = 'x', data = train, palette="Set2")
plt.xticks()
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
ax1.set_xlabel("x value", fontsize=14, labelpad=10)
ax1.set_ylabel("Count", fontsize=14, labelpad=10)
ax1.set_title('Distribution of x - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax2 = fig.add_subplot(3, 2, 2)
sns.countplot(x = 'x', data = test, palette="Set2")
plt.xticks()
ax2.spines['right'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax2.set_xlabel("x value", fontsize=14, labelpad=10)
ax2.set_ylabel("Count", fontsize=14, labelpad=10)
ax2.set_title('Distribution of x - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
# y distribution
ax3 = fig.add_subplot(3, 2, 3)
sns.countplot(x = 'y', data = train, palette="Set2")
plt.xticks()
ax3.spines['right'].set_visible(False)
ax3.spines['top'].set_visible(False)
ax3.set_xlabel("y value", fontsize=14, labelpad=10)
ax3.set_ylabel("Count", fontsize=14, labelpad=10)
ax3.set_title('Distribution of y - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax4 = fig.add_subplot(3, 2, 4)
sns.countplot(x = 'y', data = test, palette="Set2")
plt.xticks()
ax4.spines['right'].set_visible(False)
ax4.spines['top'].set_visible(False)
ax4.set_xlabel("y value", fontsize=14, labelpad=10)
ax4.set_ylabel("Count", fontsize=14, labelpad=10)
ax4.set_title('Distribution of y - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
# direction distribution
ax5 = fig.add_subplot(3, 2, 5)
sns.countplot(x = 'direction', data = train, palette="Set2")
plt.xticks()
ax5.spines['right'].set_visible(False)
ax5.spines['top'].set_visible(False)
ax5.set_xlabel("direction", fontsize=14, labelpad=10)
ax5.set_ylabel("Count", fontsize=14, labelpad=10)
ax5.set_title('Distribution of direction - Train Set', loc = 'center', fontsize = 14, fontweight = 'bold')
ax6 = fig.add_subplot(3, 2, 6)
sns.countplot(x = 'direction', data = test, palette="Set2")
plt.xticks()
ax6.spines['right'].set_visible(False)
ax6.spines['top'].set_visible(False)
ax6.set_xlabel("direction", fontsize=14, labelpad=10)
ax6.set_ylabel("Count", fontsize=14, labelpad=10)
ax6.set_title('Distribution of direction - Test Set', loc = 'center', fontsize = 14, fontweight = 'bold')
fig.tight_layout()
categorical_columns = train[['x', 'y', 'direction']].columns.to_numpy()
fig = plt.figure(figsize = (10, 9))
rows = 3
cols = 1
for idx, categorical_column in enumerate(categorical_columns):
ax = fig.add_subplot(rows, cols, idx + 1)
sns.barplot(x = categorical_column, y = 'congestion', data = train.groupby(categorical_column).mean('congestion')['congestion'].reset_index(), palette = 'Set2')
ax.xaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.yaxis.set_tick_params(labelsize=10, size=0, pad=5)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_ylabel('congestion')
ax.set_xlabel(categorical_column + " value")
ax.bar_label(ax.containers[0])
ax.set_title('Average congestion by ' + categorical_column, loc = 'center', fontsize = 14, fontweight = 'bold', pad = 20)
ax.legend()
fig.tight_layout()
fig.show()
train.dtypes | code |
89141713/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
submission = pd.read_csv('/kaggle/input/tabular-playground-series-mar-2022/test.csv')
train.isnull().sum()
train.describe() | code |
89141713/cell_5 | [
"image_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from scipy.stats import mode
from xgboost import XGBClassifier
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import VotingClassifier
from datetime import datetime
import warnings
import time
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('float_format', '{:f}'.format)
warnings.filterwarnings('ignore')
RANDOM_STATE = 14
FOLDS = 5 | code |
106212246/cell_9 | [
"image_output_1.png"
] | y_train | code |
106212246/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/adl-classification/dataset.csv', names=['MQ1', 'MQ2', 'MQ3', 'MQ4', 'MQ5', 'MQ6', 'CO2'])
data.info() | code |
106212246/cell_11 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(random_state=1)
model.fit(X_train, y_train) | code |
106212246/cell_8 | [
"text_plain_output_1.png"
] | X_train | code |
106212246/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/adl-classification/dataset.csv', names=['MQ1', 'MQ2', 'MQ3', 'MQ4', 'MQ5', 'MQ6', 'CO2'])
data | code |
106212246/cell_14 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import shap
model = RandomForestClassifier(random_state=1)
model.fit(X_train, y_train)
acc = model.score(X_test, y_test)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test, class_names=model.classes_) | code |
106212246/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(random_state=1)
model.fit(X_train, y_train)
acc = model.score(X_test, y_test)
print('Accuracy {:.2f}%'.format(acc * 100)) | code |
1005815/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
import numpy as np
import pandas as pd
import scipy
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from subprocess import check_output
df = pd.read_json('../input/train.json')
df['priceperbed'] = df['price'].clip(upper=7000) / df['bedrooms'].clip(lower=1)
df['created'] = df['created'].astype(np.datetime64)
df['created_day'] = np.array(df.created.values, dtype='datetime64[D]').astype(np.float32) % 7
df['created_week'] = np.array(df.created.values, dtype='datetime64[W]').astype(np.float32)
df['created_hour'] = np.array(df.created.values, dtype='datetime64[h]').astype(np.float32) % 24
df['desc_count'] = df.description.apply(lambda x: len(x.split())).clip(upper=150)
df['features_count'] = df.features.apply(lambda x: len(x))
df['photos_count'] = df.photos.apply(lambda x: len(x))
lbl = preprocessing.LabelEncoder()
lbl.fit(list(df['manager_id'].values))
df['manager_id'] = lbl.transform(list(df['manager_id'].values))
feature_list = ['no fee', 'hardwood floors', 'laundry in building']
df['features'] = df['features'].apply(lambda x: list(map(str.lower, x)))
for feature in feature_list:
df[feature] = df['features'].apply(lambda x: feature in x)
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english')
vectorizer.fit(df.description.values)
temp = pd.concat([df_train.manager_id, pd.get_dummies(df_train.interest_level)], axis=1).groupby('manager_id').mean()
temp.columns = ['high_frac', 'low_frac', 'medium_frac']
temp['count'] = df_train.groupby('manager_id').count().iloc[:, 1]
temp['manager_skill'] = temp['high_frac'] * 2 + temp['medium_frac']
unranked_managers_ixes = temp['count'] < 8
ranked_managers_ixes = ~unranked_managers_ixes
mean_values = temp.loc[ranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']].mean()
temp.loc[unranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_train = df_train.merge(temp.reset_index(), how='left', on='manager_id')
df_val = df_val.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_val['high_frac'].isnull()
df_val.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_test = df_test.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_test['high_frac'].isnull()
df_test.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
derived_cols = ['derived_' + str(i) for i in range(5)]
cols = ['price', 'bathrooms', 'bedrooms', 'latitude', 'longitude', 'priceperbed', 'created_hour', 'desc_count', 'photos_count', 'features_count', 'no fee', 'hardwood floors', 'laundry in building', 'manager_skill']
svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
X_train = svd.fit_transform(vectorizer.transform(df_train.description))
X_train = np.hstack([X_train, df_train[cols].values])
X_val = svd.transform(vectorizer.transform(df_val.description))
X_val = np.hstack([X_val, df_val[cols].values])
X_test = svd.transform(vectorizer.transform(df_test.description))
X_test = np.hstack([X_test, df_test[cols].values])
target_num_map = {'high': 0, 'low': 1, 'medium': 2}
y_train = np.array(df_train['interest_level'].apply(lambda x: target_num_map[x]))
y_test = np.array(df_test['interest_level'].apply(lambda x: target_num_map[x]))
y_val = np.array(df_val['interest_level'].apply(lambda x: target_num_map[x]))
import xgboost as xgb
SEED = 0
params = {'eta': 0.01, 'colsample_bytree': 0.8, 'subsample': 0.8, 'seed': 0, 'nthread': 16, 'objective': 'multi:softprob', 'eval_metric': 'mlogloss', 'num_class': 3}
dtrain = xgb.DMatrix(data=X_train, label=y_train)
bst = xgb.train(params, dtrain, 1000, verbose_eval=25)
y_pred = bst.predict(dtrain)
score = log_loss(df_train['interest_level'].values, y_pred)
xgval = xgb.DMatrix(X_val)
y_pred = bst.predict(xgval)
score2 = log_loss(df_val['interest_level'].values, y_pred)
xgtest = xgb.DMatrix(X_test)
y_pred = bst.predict(xgtest)
score3 = log_loss(df_test['interest_level'].values, y_pred)
imps = bst.get_fscore()
imps_sorted = [imps[i] for i in sorted(imps.keys(), key=lambda x: int(x[1:]))]
pd.Series(index=derived_cols + cols, data=imps_sorted).sort_values().plot(kind='bar') | code |
1005815/cell_1 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.feature_extraction.text import TfidfVectorizer
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import scipy
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
df = pd.read_json('../input/train.json')
df['priceperbed'] = df['price'].clip(upper=7000) / df['bedrooms'].clip(lower=1)
df['created'] = df['created'].astype(np.datetime64)
df['created_day'] = np.array(df.created.values, dtype='datetime64[D]').astype(np.float32) % 7
df['created_week'] = np.array(df.created.values, dtype='datetime64[W]').astype(np.float32)
df['created_hour'] = np.array(df.created.values, dtype='datetime64[h]').astype(np.float32) % 24
df['desc_count'] = df.description.apply(lambda x: len(x.split())).clip(upper=150)
df['features_count'] = df.features.apply(lambda x: len(x))
df['photos_count'] = df.photos.apply(lambda x: len(x))
lbl = preprocessing.LabelEncoder()
lbl.fit(list(df['manager_id'].values))
df['manager_id'] = lbl.transform(list(df['manager_id'].values))
feature_list = ['no fee', 'hardwood floors', 'laundry in building']
df['features'] = df['features'].apply(lambda x: list(map(str.lower, x)))
for feature in feature_list:
df[feature] = df['features'].apply(lambda x: feature in x)
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english')
vectorizer.fit(df.description.values) | code |
1005815/cell_7 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
import numpy as np
import pandas as pd
import scipy
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from subprocess import check_output
df = pd.read_json('../input/train.json')
df['priceperbed'] = df['price'].clip(upper=7000) / df['bedrooms'].clip(lower=1)
df['created'] = df['created'].astype(np.datetime64)
df['created_day'] = np.array(df.created.values, dtype='datetime64[D]').astype(np.float32) % 7
df['created_week'] = np.array(df.created.values, dtype='datetime64[W]').astype(np.float32)
df['created_hour'] = np.array(df.created.values, dtype='datetime64[h]').astype(np.float32) % 24
df['desc_count'] = df.description.apply(lambda x: len(x.split())).clip(upper=150)
df['features_count'] = df.features.apply(lambda x: len(x))
df['photos_count'] = df.photos.apply(lambda x: len(x))
lbl = preprocessing.LabelEncoder()
lbl.fit(list(df['manager_id'].values))
df['manager_id'] = lbl.transform(list(df['manager_id'].values))
feature_list = ['no fee', 'hardwood floors', 'laundry in building']
df['features'] = df['features'].apply(lambda x: list(map(str.lower, x)))
for feature in feature_list:
df[feature] = df['features'].apply(lambda x: feature in x)
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english')
vectorizer.fit(df.description.values)
temp = pd.concat([df_train.manager_id, pd.get_dummies(df_train.interest_level)], axis=1).groupby('manager_id').mean()
temp.columns = ['high_frac', 'low_frac', 'medium_frac']
temp['count'] = df_train.groupby('manager_id').count().iloc[:, 1]
temp['manager_skill'] = temp['high_frac'] * 2 + temp['medium_frac']
unranked_managers_ixes = temp['count'] < 8
ranked_managers_ixes = ~unranked_managers_ixes
mean_values = temp.loc[ranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']].mean()
temp.loc[unranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_train = df_train.merge(temp.reset_index(), how='left', on='manager_id')
df_val = df_val.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_val['high_frac'].isnull()
df_val.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_test = df_test.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_test['high_frac'].isnull()
df_test.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
derived_cols = ['derived_' + str(i) for i in range(5)]
cols = ['price', 'bathrooms', 'bedrooms', 'latitude', 'longitude', 'priceperbed', 'created_hour', 'desc_count', 'photos_count', 'features_count', 'no fee', 'hardwood floors', 'laundry in building', 'manager_skill']
svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
X_train = svd.fit_transform(vectorizer.transform(df_train.description))
X_train = np.hstack([X_train, df_train[cols].values])
X_val = svd.transform(vectorizer.transform(df_val.description))
X_val = np.hstack([X_val, df_val[cols].values])
X_test = svd.transform(vectorizer.transform(df_test.description))
X_test = np.hstack([X_test, df_test[cols].values])
target_num_map = {'high': 0, 'low': 1, 'medium': 2}
y_train = np.array(df_train['interest_level'].apply(lambda x: target_num_map[x]))
y_test = np.array(df_test['interest_level'].apply(lambda x: target_num_map[x]))
y_val = np.array(df_val['interest_level'].apply(lambda x: target_num_map[x]))
import xgboost as xgb
SEED = 0
params = {'eta': 0.01, 'colsample_bytree': 0.8, 'subsample': 0.8, 'seed': 0, 'nthread': 16, 'objective': 'multi:softprob', 'eval_metric': 'mlogloss', 'num_class': 3}
dtrain = xgb.DMatrix(data=X_train, label=y_train)
bst = xgb.train(params, dtrain, 1000, verbose_eval=25)
y_pred = bst.predict(dtrain)
score = log_loss(df_train['interest_level'].values, y_pred)
xgval = xgb.DMatrix(X_val)
y_pred = bst.predict(xgval)
score2 = log_loss(df_val['interest_level'].values, y_pred)
xgtest = xgb.DMatrix(X_test)
y_pred = bst.predict(xgtest)
score3 = log_loss(df_test['interest_level'].values, y_pred)
imps = bst.get_fscore()
imps_sorted = [imps[i] for i in sorted(imps.keys(), key=lambda x: int(x[1:]))]
pd.Series(index=derived_cols + cols, data=imps_sorted).sort_values().plot(kind='bar')
imps_sorted | code |
1005815/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
import numpy as np
import pandas as pd
import scipy
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from subprocess import check_output
df = pd.read_json('../input/train.json')
df['priceperbed'] = df['price'].clip(upper=7000) / df['bedrooms'].clip(lower=1)
df['created'] = df['created'].astype(np.datetime64)
df['created_day'] = np.array(df.created.values, dtype='datetime64[D]').astype(np.float32) % 7
df['created_week'] = np.array(df.created.values, dtype='datetime64[W]').astype(np.float32)
df['created_hour'] = np.array(df.created.values, dtype='datetime64[h]').astype(np.float32) % 24
df['desc_count'] = df.description.apply(lambda x: len(x.split())).clip(upper=150)
df['features_count'] = df.features.apply(lambda x: len(x))
df['photos_count'] = df.photos.apply(lambda x: len(x))
lbl = preprocessing.LabelEncoder()
lbl.fit(list(df['manager_id'].values))
df['manager_id'] = lbl.transform(list(df['manager_id'].values))
feature_list = ['no fee', 'hardwood floors', 'laundry in building']
df['features'] = df['features'].apply(lambda x: list(map(str.lower, x)))
for feature in feature_list:
df[feature] = df['features'].apply(lambda x: feature in x)
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english')
vectorizer.fit(df.description.values)
temp = pd.concat([df_train.manager_id, pd.get_dummies(df_train.interest_level)], axis=1).groupby('manager_id').mean()
temp.columns = ['high_frac', 'low_frac', 'medium_frac']
temp['count'] = df_train.groupby('manager_id').count().iloc[:, 1]
temp['manager_skill'] = temp['high_frac'] * 2 + temp['medium_frac']
unranked_managers_ixes = temp['count'] < 8
ranked_managers_ixes = ~unranked_managers_ixes
mean_values = temp.loc[ranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']].mean()
temp.loc[unranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_train = df_train.merge(temp.reset_index(), how='left', on='manager_id')
df_val = df_val.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_val['high_frac'].isnull()
df_val.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_test = df_test.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_test['high_frac'].isnull()
df_test.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
derived_cols = ['derived_' + str(i) for i in range(5)]
cols = ['price', 'bathrooms', 'bedrooms', 'latitude', 'longitude', 'priceperbed', 'created_hour', 'desc_count', 'photos_count', 'features_count', 'no fee', 'hardwood floors', 'laundry in building', 'manager_skill']
svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
X_train = svd.fit_transform(vectorizer.transform(df_train.description))
X_train = np.hstack([X_train, df_train[cols].values])
X_val = svd.transform(vectorizer.transform(df_val.description))
X_val = np.hstack([X_val, df_val[cols].values])
X_test = svd.transform(vectorizer.transform(df_test.description))
X_test = np.hstack([X_test, df_test[cols].values])
target_num_map = {'high': 0, 'low': 1, 'medium': 2}
y_train = np.array(df_train['interest_level'].apply(lambda x: target_num_map[x]))
y_test = np.array(df_test['interest_level'].apply(lambda x: target_num_map[x]))
y_val = np.array(df_val['interest_level'].apply(lambda x: target_num_map[x]))
import xgboost as xgb
SEED = 0
params = {'eta': 0.01, 'colsample_bytree': 0.8, 'subsample': 0.8, 'seed': 0, 'nthread': 16, 'objective': 'multi:softprob', 'eval_metric': 'mlogloss', 'num_class': 3}
dtrain = xgb.DMatrix(data=X_train, label=y_train)
bst = xgb.train(params, dtrain, 1000, verbose_eval=25)
y_pred = bst.predict(dtrain)
score = log_loss(df_train['interest_level'].values, y_pred)
xgval = xgb.DMatrix(X_val)
y_pred = bst.predict(xgval)
score2 = log_loss(df_val['interest_level'].values, y_pred)
xgtest = xgb.DMatrix(X_test)
y_pred = bst.predict(xgtest)
score3 = log_loss(df_test['interest_level'].values, y_pred)
imps = bst.get_fscore()
imps_sorted = [imps[i] for i in sorted(imps.keys(), key=lambda x: int(x[1:]))]
pd.Series(index=derived_cols + cols, data=imps_sorted).sort_values().plot(kind='bar')
imps | code |
1005815/cell_5 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import log_loss
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
import numpy as np
import pandas as pd
import scipy
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.naive_bayes import BernoulliNB, MultinomialNB
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import log_loss
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from subprocess import check_output
df = pd.read_json('../input/train.json')
df['priceperbed'] = df['price'].clip(upper=7000) / df['bedrooms'].clip(lower=1)
df['created'] = df['created'].astype(np.datetime64)
df['created_day'] = np.array(df.created.values, dtype='datetime64[D]').astype(np.float32) % 7
df['created_week'] = np.array(df.created.values, dtype='datetime64[W]').astype(np.float32)
df['created_hour'] = np.array(df.created.values, dtype='datetime64[h]').astype(np.float32) % 24
df['desc_count'] = df.description.apply(lambda x: len(x.split())).clip(upper=150)
df['features_count'] = df.features.apply(lambda x: len(x))
df['photos_count'] = df.photos.apply(lambda x: len(x))
lbl = preprocessing.LabelEncoder()
lbl.fit(list(df['manager_id'].values))
df['manager_id'] = lbl.transform(list(df['manager_id'].values))
feature_list = ['no fee', 'hardwood floors', 'laundry in building']
df['features'] = df['features'].apply(lambda x: list(map(str.lower, x)))
for feature in feature_list:
df[feature] = df['features'].apply(lambda x: feature in x)
vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english')
vectorizer.fit(df.description.values)
temp = pd.concat([df_train.manager_id, pd.get_dummies(df_train.interest_level)], axis=1).groupby('manager_id').mean()
temp.columns = ['high_frac', 'low_frac', 'medium_frac']
temp['count'] = df_train.groupby('manager_id').count().iloc[:, 1]
temp['manager_skill'] = temp['high_frac'] * 2 + temp['medium_frac']
unranked_managers_ixes = temp['count'] < 8
ranked_managers_ixes = ~unranked_managers_ixes
mean_values = temp.loc[ranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']].mean()
temp.loc[unranked_managers_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_train = df_train.merge(temp.reset_index(), how='left', on='manager_id')
df_val = df_val.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_val['high_frac'].isnull()
df_val.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
df_test = df_test.merge(temp.reset_index(), how='left', on='manager_id')
new_manager_ixes = df_test['high_frac'].isnull()
df_test.loc[new_manager_ixes, ['high_frac', 'low_frac', 'medium_frac', 'manager_skill']] = mean_values.values
derived_cols = ['derived_' + str(i) for i in range(5)]
cols = ['price', 'bathrooms', 'bedrooms', 'latitude', 'longitude', 'priceperbed', 'created_hour', 'desc_count', 'photos_count', 'features_count', 'no fee', 'hardwood floors', 'laundry in building', 'manager_skill']
svd = TruncatedSVD(n_components=5, n_iter=7, random_state=42)
X_train = svd.fit_transform(vectorizer.transform(df_train.description))
X_train = np.hstack([X_train, df_train[cols].values])
X_val = svd.transform(vectorizer.transform(df_val.description))
X_val = np.hstack([X_val, df_val[cols].values])
X_test = svd.transform(vectorizer.transform(df_test.description))
X_test = np.hstack([X_test, df_test[cols].values])
target_num_map = {'high': 0, 'low': 1, 'medium': 2}
y_train = np.array(df_train['interest_level'].apply(lambda x: target_num_map[x]))
y_test = np.array(df_test['interest_level'].apply(lambda x: target_num_map[x]))
y_val = np.array(df_val['interest_level'].apply(lambda x: target_num_map[x]))
import xgboost as xgb
SEED = 0
params = {'eta': 0.01, 'colsample_bytree': 0.8, 'subsample': 0.8, 'seed': 0, 'nthread': 16, 'objective': 'multi:softprob', 'eval_metric': 'mlogloss', 'num_class': 3}
dtrain = xgb.DMatrix(data=X_train, label=y_train)
bst = xgb.train(params, dtrain, 1000, verbose_eval=25)
y_pred = bst.predict(dtrain)
score = log_loss(df_train['interest_level'].values, y_pred)
xgval = xgb.DMatrix(X_val)
y_pred = bst.predict(xgval)
score2 = log_loss(df_val['interest_level'].values, y_pred)
xgtest = xgb.DMatrix(X_test)
y_pred = bst.predict(xgtest)
score3 = log_loss(df_test['interest_level'].values, y_pred)
print('%.6f %.6f %.6f' % (score, score2, score3)) | code |
105204314/cell_4 | [
"text_plain_output_1.png"
] | from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
dftr = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//train.csv')
dftr['src'] = 'train'
dfte = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//test.csv')
dfte['src'] = 'test'
df = pd.concat([dftr, dfte], ignore_index=True)
target_cols = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']
import sys
sys.path.append('../input/iterativestratification')
from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
FOLDS = 5
skf = MultilabelStratifiedKFold(n_splits=FOLDS, shuffle=True, random_state=2022)
for i, (train_index, val_index) in enumerate(skf.split(dftr, dftr[target_cols])):
dftr.loc[val_index, 'FOLD'] = i
dftr.FOLD.value_counts() | code |
105204314/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dftr = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//train.csv')
dftr['src'] = 'train'
dfte = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//test.csv')
dfte['src'] = 'test'
print(dftr.shape, dfte.shape, dfte.columns)
df = pd.concat([dftr, dfte], ignore_index=True)
dftr.head() | code |
105204314/cell_1 | [
"text_plain_output_1.png"
] | import os
import warnings
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import re
import warnings
def fxn():
warnings.warn('deprecated', DeprecationWarning)
with warnings.catch_warnings():
warnings.simplefilter('ignore')
fxn() | code |
105204314/cell_5 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LinearRegression,SGDRegressor
from sklearn.metrics import mean_squared_error
from sklearn.multioutput import MultiOutputRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
dftr = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//train.csv')
dftr['src'] = 'train'
dfte = pd.read_csv('/kaggle/input/feedback-prize-english-language-learning//test.csv')
dfte['src'] = 'test'
df = pd.concat([dftr, dfte], ignore_index=True)
target_cols = ['cohesion', 'syntax', 'vocabulary', 'phraseology', 'grammar', 'conventions']
import sys
sys.path.append('../input/iterativestratification')
from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
FOLDS = 5
skf = MultilabelStratifiedKFold(n_splits=FOLDS, shuffle=True, random_state=2022)
for i, (train_index, val_index) in enumerate(skf.split(dftr, dftr[target_cols])):
dftr.loc[val_index, 'FOLD'] = i
dftr.FOLD.value_counts()
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LinearRegression, SGDRegressor
from scipy import sparse
from sklearn.multioutput import MultiOutputRegressor
from scipy.stats import pearsonr
from sklearn.metrics import mean_squared_error
preds = []
scores = []
def comp_score(y_true, y_pred):
rmse_scores = []
for i in range(len(target_cols)):
rmse_scores.append(np.sqrt(mean_squared_error(y_true[:, i], y_pred[:, i])))
return np.mean(rmse_scores)
for fold in range(FOLDS):
dftr_ = dftr[dftr['FOLD'] != fold]
dfev_ = dftr[dftr['FOLD'] == fold]
tf = TfidfVectorizer(ngram_range=(1, 2))
tf = tf.fit(dftr_['full_text'])
tr_text_feats = tf.transform(dftr_['full_text'])
ev_text_feats = tf.transform(dfev_['full_text'])
te_text_feats = tf.transform(dfte['full_text'])
clf = MultiOutputRegressor(LinearRegression(n_jobs=-1, normalize=True))
clf.fit(tr_text_feats, dftr_[target_cols].values)
ev_preds = clf.predict(ev_text_feats)
score = comp_score(dfev_[target_cols].values, ev_preds)
scores.append(score)
print('Fold : {} EV score: {}'.format(fold, score))
preds.append(clf.predict(te_text_feats)) | code |
327528/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import math
import numpy as np
from keras.layers import Input
from keras import backend as K
from keras.engine.topology import Layer
from skimage.util.montage import montage2d | code |
327528/cell_5 | [
"image_output_1.png"
] | from IPython.display import display, Image
from PIL.Image import fromarray
from io import BytesIO
from keras.engine.topology import Layer
from keras.layers import Input
from numpy import asarray, uint8, clip
from skimage.util.montage import montage2d
import math
import numpy as np
def nbimage(data, vmin=None, vmax=None, vsym=False, saveas=None):
"""
Display raw data as a notebook inline image.
Parameters:
data: array-like object, two or three dimensions. If three dimensional,
first or last dimension must have length 3 or 4 and will be
interpreted as color (RGB or RGBA).
vmin, vmax, vsym: refer to rerange()
saveas: Save image file to disk (optional). Proper file name extension
will be appended to the pathname given. [ None ]
"""
from IPython.display import display, Image
from PIL.Image import fromarray
from io import BytesIO
data = rerange(data, vmin, vmax, vsym)
data = data.squeeze()
if data.ndim == 3 and 3 <= data.shape[0] <= 4:
data = data.transpose((1, 2, 0))
s = BytesIO()
fromarray(data).save(s, 'png')
if saveas is not None:
open(saveas + '.png', 'wb').write(s)
def rerange(data, vmin=None, vmax=None, vsym=False):
"""
Rescale values of data array to fit the range 0 ... 255 and convert to uint8.
Parameters:
data: array-like object. if data.dtype == uint8, no scaling will occur.
vmin: original array value that will map to 0 in the output. [ data.min() ]
vmax: original array value that will map to 255 in the output. [ data.max() ]
vsym: ensure that 0 will map to gray (if True, may override either vmin or vmax
to accommodate all values.) [ False ]
"""
from numpy import asarray, uint8, clip
data = asarray(data)
if data.dtype != uint8:
if vmin is None:
vmin = data.min()
if vmax is None:
vmax = data.max()
if vsym:
vmax = max(abs(vmin), abs(vmax))
vmin = -vmax
data = (data - vmin) * (256 / (vmax - vmin))
data = clip(data, 0, 255).astype(uint8)
return data
class Gaussian2D(Layer):
def __init__(self, output_shape, **kwargs):
self.output_shape_ = output_shape
self.height = output_shape[2]
self.width = output_shape[3]
self.grid = np.dstack(np.mgrid[-1:1:2.0 / self.height, -1:1:2.0 / self.width])[None, ...]
super(Gaussian2D, self).__init__(**kwargs)
def call(self, inputs, mask=None):
mu, sigma, corr, scale = inputs
mu = K.tanh(mu) * 0.95
sigma = K.exp(sigma) + 1e-05
corr = K.tanh(corr[:, 0]) * 0.95
scale = K.exp(scale[:, 0])
mu0 = K.permute_dimensions(mu[:, 0], (0, 'x', 'x', 'x'))
mu1 = K.permute_dimensions(mu[:, 1], (0, 'x', 'x', 'x'))
sigma0 = K.permute_dimensions(sigma[:, 0], (0, 'x', 'x', 'x'))
sigma1 = K.permute_dimensions(sigma[:, 1], (0, 'x', 'x', 'x'))
grid0 = self.grid[..., 0]
grid1 = self.grid[..., 1]
corr = K.permute_dimensions(corr, (0, 'x', 'x', 'x'))
scale = K.permute_dimensions(scale, (0, 'x', 'x', 'x'))
return K.tanh(scale / (2.0 * math.pi * sigma0 * sigma1 * K.sqrt(1.0 - corr * corr)) * K.exp(-(1.0 / (2.0 * (1.0 - corr * corr)) * ((grid0 - mu0) * (grid0 - mu0) / (sigma0 * sigma0) + (grid1 - mu1) * (grid1 - mu1) / (sigma1 * sigma1) - 2.0 * corr * (grid0 - mu0) * (grid1 - mu1) / sigma0 / sigma1))))
def get_output_shape_for(self, input_shape):
return self.output_shape_
mu_input = Input((2,))
sigma_input = Input((2,))
corr_input = Input((1,))
scale_input = Input((1,))
g = Gaussian2D(output_shape=(None, 1, 100, 100))([mu_input, sigma_input, corr_input, scale_input])
n = 3 * 3
mu = np.random.normal(size=(n, 2)) / 3
sigma = np.random.uniform(-3, -2, size=(n, 2))
corr = np.random.normal(size=(n, 1)) / 5
scale = np.random.normal(size=(n, 1))
gaussians = g.eval({mu_input: mu.astype('float32'), sigma_input: sigma.astype('float32'), corr_input: corr.astype('float32'), scale_input: scale.astype('float32')})
nbimage(montage2d(gaussians.squeeze().clip(0, 1))) | code |
122249691/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='validation', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
class_names = train_ds.class_names
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(6):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
resnet_model = Sequential()
pretrained_model = tf.keras.applications.ResNet50(include_top=False, input_shape=(180, 180, 3), pooling='avg', classes=5, weights='imagenet')
for layer in pretrained_model.layers:
layer.trainable = False
resnet_model.add(pretrained_model)
resnet_model.add(Flatten())
resnet_model.add(Dense(512, activation='relu'))
resnet_model.add(Dense(5, activation='softmax'))
resnet_model.summary()
resnet_model.compile(optimizer=Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
epochs = 10
history = resnet_model.fit(train_ds, validation_data=val_ds, epochs=epochs) | code |
122249691/cell_9 | [
"text_plain_output_1.png"
] | import PIL
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
class_names = train_ds.class_names
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(6):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype('uint8'))
plt.title(class_names[labels[i]])
plt.axis('off') | code |
122249691/cell_4 | [
"text_plain_output_1.png"
] | import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
print(data_dir) | code |
122249691/cell_6 | [
"image_output_1.png"
] | import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size) | code |
122249691/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='validation', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
resnet_model = Sequential()
pretrained_model = tf.keras.applications.ResNet50(include_top=False, input_shape=(180, 180, 3), pooling='avg', classes=5, weights='imagenet')
for layer in pretrained_model.layers:
layer.trainable = False
resnet_model.add(pretrained_model)
resnet_model.add(Flatten())
resnet_model.add(Dense(512, activation='relu'))
resnet_model.add(Dense(5, activation='softmax'))
resnet_model.summary() | code |
122249691/cell_7 | [
"text_plain_output_1.png"
] | import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='validation', seed=123, image_size=(img_height, img_width), batch_size=batch_size) | code |
122249691/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='validation', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
class_names = train_ds.class_names
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(6):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
resnet_model = Sequential()
pretrained_model = tf.keras.applications.ResNet50(include_top=False, input_shape=(180, 180, 3), pooling='avg', classes=5, weights='imagenet')
for layer in pretrained_model.layers:
layer.trainable = False
resnet_model.add(pretrained_model)
resnet_model.add(Flatten())
resnet_model.add(Dense(512, activation='relu'))
resnet_model.add(Dense(5, activation='softmax'))
resnet_model.summary()
resnet_model.compile(optimizer=Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
epochs = 10
history = resnet_model.fit(train_ds, validation_data=val_ds, epochs=epochs)
import cv2
image = cv2.imread(str(roses[0]))
image_resized = cv2.resize(image, (img_height, img_width))
image = np.expand_dims(image_resized, axis=0)
pred = resnet_model.predict(image)
output_class = class_names[np.argmax(pred)]
print('The predicted class is', output_class) | code |
122249691/cell_8 | [
"text_plain_output_1.png"
] | import PIL
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
class_names = train_ds.class_names
print(class_names) | code |
122249691/cell_15 | [
"text_plain_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='validation', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
class_names = train_ds.class_names
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(6):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
resnet_model = Sequential()
pretrained_model = tf.keras.applications.ResNet50(include_top=False, input_shape=(180, 180, 3), pooling='avg', classes=5, weights='imagenet')
for layer in pretrained_model.layers:
layer.trainable = False
resnet_model.add(pretrained_model)
resnet_model.add(Flatten())
resnet_model.add(Dense(512, activation='relu'))
resnet_model.add(Dense(5, activation='softmax'))
resnet_model.summary()
resnet_model.compile(optimizer=Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
epochs = 10
history = resnet_model.fit(train_ds, validation_data=val_ds, epochs=epochs)
fig1 = plt.gcf()
plt.axis(ymin=0.4, ymax=1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.grid()
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epochs')
plt.legend(['train', 'validation'])
plt.show() | code |
122249691/cell_16 | [
"text_plain_output_1.png"
] | import PIL
import cv2
import numpy as np # linear algebra
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
import cv2
image = cv2.imread(str(roses[0]))
image_resized = cv2.resize(image, (img_height, img_width))
image = np.expand_dims(image_resized, axis=0)
print(image.shape) | code |
122249691/cell_3 | [
"text_plain_output_1.png"
] | import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir) | code |
122249691/cell_17 | [
"image_output_1.png"
] | from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.python.keras.layers import Dense, Flatten
import PIL
import cv2
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pathlib
import tensorflow as tf
import pathlib
dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
img_height, img_width = (180, 180)
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='training', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir, validation_split=0.2, subset='validation', seed=123, image_size=(img_height, img_width), batch_size=batch_size)
class_names = train_ds.class_names
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(6):
ax = plt.subplot(3, 3, i + 1)
plt.imshow(images[i].numpy().astype("uint8"))
plt.title(class_names[labels[i]])
plt.axis("off")
resnet_model = Sequential()
pretrained_model = tf.keras.applications.ResNet50(include_top=False, input_shape=(180, 180, 3), pooling='avg', classes=5, weights='imagenet')
for layer in pretrained_model.layers:
layer.trainable = False
resnet_model.add(pretrained_model)
resnet_model.add(Flatten())
resnet_model.add(Dense(512, activation='relu'))
resnet_model.add(Dense(5, activation='softmax'))
resnet_model.summary()
resnet_model.compile(optimizer=Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
epochs = 10
history = resnet_model.fit(train_ds, validation_data=val_ds, epochs=epochs)
import cv2
image = cv2.imread(str(roses[0]))
image_resized = cv2.resize(image, (img_height, img_width))
image = np.expand_dims(image_resized, axis=0)
pred = resnet_model.predict(image)
print(pred) | code |
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