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import pandas as pd | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.metrics import f1_score | |
from sklearn.model_selection import train_test_split | |
# Importing dataset | |
df=pd.read_csv('dataset.csv') | |
df.head() |
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# Creating feature groups | |
f1=folium.FeatureGroup("Vehicle 1") | |
f2=folium.FeatureGroup("Vehicle 2") | |
f3=folium.FeatureGroup("Vehicle 3") | |
# Adding lines to the different feature groups | |
line_1=folium.vector_layers.PolyLine(coords_1,popup='<b>Path of Vehicle_1</b>',tooltip='Vehicle_1',color='blue',weight=10).add_to(f1) | |
line_2=folium.vector_layers.PolyLine(coords_2,popup='<b>Path of Vehicle_2</b>',tooltip='Vehicle_2',color='red',weight=10).add_to(f2) | |
line_3=folium.vector_layers.PolyLine(coords_3,popup='<b>Path of Vehicle_3</b>',tooltip='Vehicle_3',color='green',weight=10).add_to(f3) |
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# Combining corpus and queries for training | |
combined_training=pd.concat([training_corpus.rename(columns={'lemmatized':'text'})['text'],\ | |
training_queries.rename(columns={'cleaned':'text'})['text']])\ | |
.sample(frac=1).reset_index(drop=True) |
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fig2=Figure(width=550,height=350) | |
m2=folium.Map(location=[28.644800, 77.216721]) | |
fig2.add_child(m2) | |
folium.TileLayer('Stamen Terrain').add_to(m2) | |
folium.TileLayer('Stamen Toner').add_to(m2) | |
folium.TileLayer('Stamen Water Color').add_to(m2) | |
folium.TileLayer('cartodbpositron').add_to(m2) | |
folium.TileLayer('cartodbdark_matter').add_to(m2) | |
folium.LayerControl().add_to(m2) | |
m2 |
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# Evaluating on test set | |
test_precision,test_recall,test_Fmeasure=evaluate_micro_average(df_test['keys'].values,df_test['pred_keys']) | |
print('Precision=>',test_precision) | |
print('Recall=>',test_recall) | |
print('F-measure=>',test_Fmeasure) |
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from folium.plugins import HeatMapWithTime | |
fig7=Figure(width=850,height=550) | |
m7=folium.Map(location=[40.712776, -74.005974],zoom_start=10) | |
fig7.add_child(m7) |
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# Creating Document Term Matrix | |
from sklearn.feature_extraction.text | |
import CountVectorizer | |
cv=CountVectorizer(analyzer='word') | |
data=cv.fit_transform(df_grouped['lemmatized']) | |
df_dtm = pd.DataFrame(data.toarray(), columns=cv.get_feature_names()) | |
df_dtm.index=df_grouped.index | |
df_dtm.head(3) |
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import pandas as pd | |
df=pd.read_csv('kepler.gl-data/nyctrips/data.csv') | |
print('Shape=>',df.shape) | |
df.head() |
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# Evaluating on validation set | |
val_precision,val_recall,val_Fmeasure=evaluate_micro_average(df_val['keys'].values,df_val['pred_keys']) | |
print('Precision=>',val_precision) | |
print('Recall=>',val_recall) | |
print('F-measure=>',val_Fmeasure) |
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def evaluate_micro_average(actual_keys,predicted_keys): | |
# Combining actual keywords | |
ground_truth=[] | |
for i in actual_keys: | |
ground_truth.extend(i) | |
# Combining extracted keywords | |
extracted_keywords=[] | |
for i in predicted_keys: | |
extracted_keywords.extend(i) |
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