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stringlengths 13
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|---|---|---|---|
32068402/cell_58 | [
"text_plain_output_1.png"
] | from datetime import datetime
from gensim.models.phrases import Phraser
from pprint import pprint
from sklearn.preprocessing import normalize
from typing import List
import contractions
import ftfy
import gensim.models.keyedvectors as word2vec
import numpy as np
import operator
import os
import pandas as pd
import re
import string
import re
CURRENCIES = {'$': 'USD', 'zł': 'PLN', '£': 'GBP', '¥': 'JPY', '฿': 'THB', '₡': 'CRC', '₦': 'NGN', '₩': 'KRW', '₪': 'ILS', '₫': 'VND', '€': 'EUR', '₱': 'PHP', '₲': 'PYG', '₴': 'UAH', '₹': 'INR'}
RE_NUMBER = re.compile('(?:^|(?<=[^\\w,.]))[+–-]?(([1-9]\\d{0,2}(,\\d{3})+(\\.\\d*)?)|([1-9]\\d{0,2}([ .]\\d{3})+(,\\d*)?)|(\\d*?[.,]\\d+)|\\d+)(?:$|(?=\\b))')
RE_URL = re.compile('((http://www\\.|https://www\\.|http://|https://)?' + '[a-z0-9]+([\\-.][a-z0-9]+)*\\.[a-z]{2,5}(:[0-9]{1,5})?(/.*)?)')
STOP_WORDS = {'a', 'an', 'and', 'are', 'as', 'at', 'be', 'but', 'by', 'for', 'if', 'in', 'into', 'is', 'it', 'no', 'not', 'of', 'on', 'or', 'such', 'that', 'the', 'their', 'then', 'there', 'these', 'they', 'this', 'to', 'was', 'will', 'with'}
import string
from typing import List
import ftfy
import contractions
def clean_tokenized_sentence(tokens: List[str], unicode_normalization='NFC', unpack_contractions=False, replace_currency_symbols=False, remove_punct=True, remove_numbers=False, lowercase=True, remove_urls=True, remove_stop_words=True) -> str:
if remove_stop_words:
tokens = [token for token in tokens if token not in STOP_WORDS]
sentence = ' '.join(tokens)
if unicode_normalization:
sentence = ftfy.fix_text(sentence, normalization=unicode_normalization)
if unpack_contractions:
sentence = contractions.fix(sentence, slang=False)
if replace_currency_symbols:
for currency_sign, currency_tok in CURRENCIES.items():
sentence = sentence.replace(currency_sign, f'{currency_tok} ')
if remove_urls:
sentence = RE_URL.sub('_URL_', sentence)
if remove_punct:
sentence = sentence.translate(str.maketrans('', '', string.punctuation))
sentence = re.sub(' +', ' ', sentence)
if remove_numbers:
sentence = RE_NUMBER.sub('_NUMBER_', sentence)
if lowercase:
sentence = sentence.lower()
return sentence
sentences_df = pd.read_csv('../input/covid19sentencesmetadata/sentences_with_metadata.csv')
bigram_model = Phraser.load('../input/covid19phrasesmodels/covid_bigram_model_v0.pkl')
trigram_model = Phraser.load('../input/covid19phrasesmodels/covid_trigram_model_v0.pkl')
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'), 'r') as f:
for line_num, line in enumerate(f):
if first_line:
dim = int(line.strip().split()[1])
word_vecs = np.zeros((num_points, dim), dtype=float)
first_line = False
continue
line = line.strip()
word = line.split()[0]
vec = word_vecs[line_num - 1]
for index, vec_val in enumerate(line.split()[1:]):
vec[index] = float(vec_val)
index_to_word.append(word)
if line_num >= num_points:
break
word_vecs = normalize(word_vecs, copy=False, return_norm=False)
from pprint import pprint
import gensim.models.keyedvectors as word2vec
fasttext_model = word2vec.KeyedVectors.load_word2vec_format(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'))
def print_most_similar(search_term):
synonyms = fasttext_model.most_similar(search_term)
def create_articles_metadata_mapping(sentences_df: pd.DataFrame) -> dict:
sentence_id_to_metadata = {}
for row_count, row in sentences_df.iterrows():
sentence_id_to_metadata[row_count] = dict(paper_id=row['paper_id'], cord_uid=row['cord_uid'], source=row['source'], url=row['url'], publish_time=row['publish_time'], authors=row['authors'], section=row['section'], sentence=row['sentence'])
return sentence_id_to_metadata
sentence_id_to_metadata = create_articles_metadata_mapping(sentences_df)
import operator
from datetime import datetime
class SearchEngine:
def __init__(self, sentence_id_to_metadata: dict, sentences_df: pd.DataFrame, bigram_model, trigram_model, fasttext_model):
self.sentence_id_to_metadata = sentence_id_to_metadata
self.cleaned_sentences = sentences_df['cleaned_sentence'].tolist()
self.bigram_model = bigram_model
self.trigram_model = trigram_model
self.fasttext_model = fasttext_model
def _get_search_terms(self, keywords, synonyms_threshold):
cleaned_terms = [clean_tokenized_sentence(keyword.split(' ')) for keyword in keywords]
cleaned_terms = [term for term in cleaned_terms if term]
terms_with_bigrams = self.bigram_model[' '.join(cleaned_terms).split(' ')]
terms_with_trigrams = self.trigram_model[terms_with_bigrams]
search_terms = [self.fasttext_model.most_similar(token) for token in terms_with_trigrams]
search_terms = [synonym[0] for synonyms in search_terms for synonym in synonyms if synonym[1] >= synonyms_threshold]
search_terms = list(terms_with_trigrams) + search_terms
return search_terms
def search(self, keywords: List[str], optional_keywords=None, top_n: int=10, synonyms_threshold=0.7, keyword_weight: float=3.0, optional_keyword_weight: float=0.5) -> List[dict]:
if optional_keywords is None:
optional_keywords = []
search_terms = self._get_search_terms(keywords, synonyms_threshold)
optional_search_terms = self._get_search_terms(optional_keywords, synonyms_threshold) if optional_keywords else []
date_today = datetime.today()
indexes = []
match_counts = []
days_diffs = []
for sentence_index, sentence in enumerate(self.cleaned_sentences):
sentence_tokens = sentence.split(' ')
sentence_tokens_set = set(sentence_tokens)
match_count = sum([keyword_weight if keyword in sentence_tokens_set else 0 for keyword in search_terms])
if match_count > 0:
indexes.append(sentence_index)
if optional_search_terms:
match_count += sum([optional_keyword_weight if keyword in sentence_tokens_set else 0 for keyword in optional_search_terms])
match_counts.append(match_count)
article_date = self.sentence_id_to_metadata[sentence_index]['publish_time']
if article_date == '2020':
article_date = '2020-01-01'
article_date = datetime.strptime(article_date, '%Y-%m-%d')
days_diff = (date_today - article_date).days
days_diffs.append(days_diff)
match_counts = [float(match_count) / sum(match_counts) for match_count in match_counts]
days_diffs = [max(days_diffs) - days_diff for days_diff in days_diffs]
days_diffs = [float(days_diff) / sum(days_diffs) for days_diff in days_diffs]
index_to_score = {}
for index, match_count, days_diff in zip(indexes, match_counts, days_diffs):
index_to_score[index] = 0.7 * match_count + 0.3 * days_diff
sorted_indexes = sorted(index_to_score.items(), key=operator.itemgetter(1), reverse=True)
sorted_indexes = [item[0] for item in sorted_indexes]
sorted_indexes = sorted_indexes[0:min(top_n, len(sorted_indexes))]
results = []
for index in sorted_indexes:
results.append(self.sentence_id_to_metadata[index])
return results
search_engine = SearchEngine(sentence_id_to_metadata, sentences_df, bigram_model, trigram_model, fasttext_model)
def search(keywords, optional_keywords=None, top_n=10, synonyms_threshold=0.8, only_sentences=False):
results = search_engine.search(keywords, optional_keywords=optional_keywords, top_n=top_n, synonyms_threshold=synonyms_threshold)
search(keywords=['spillover', 'bats', 'snakes', 'exotic animals', 'seafood'], optional_keywords=['new coronavirus', 'coronavirus', 'covid19'], top_n=3) | code |
32068402/cell_28 | [
"text_plain_output_1.png"
] | from gensim.models.phrases import Phraser
bigram_model = Phraser.load('../input/covid19phrasesmodels/covid_bigram_model_v0.pkl')
bigram_model['despite social media often vehicle fake news boast news hype also worth noting tremendous effort scientific community provide free uptodate information ongoing studies well critical evaluations'.split()] | code |
32068402/cell_8 | [
"text_html_output_10.png",
"text_html_output_22.png",
"text_html_output_16.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_15.png",
"text_html_output_5.png",
"text_html_output_14.png",
"text_html_output_23.png",
"text_html_output_19.png",
"text_html_output_9.png",
"text_html_output_13.png",
"text_html_output_20.png",
"text_html_output_21.png",
"text_html_output_1.png",
"text_html_output_17.png",
"text_html_output_18.png",
"text_html_output_12.png",
"text_html_output_11.png",
"text_html_output_24.png",
"text_html_output_8.png",
"text_html_output_25.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | ['2019-ncov', '2019 novel coronavirus', 'coronavirus 2019', 'coronavirus disease 19', 'covid-19', 'covid 19', 'ncov-2019', 'sars-cov-2', 'wuhan coronavirus', 'wuhan pneumonia', 'wuhan virus'] | code |
32068402/cell_80 | [
"text_html_output_1.png"
] | from IPython.display import display, HTML
from datetime import datetime
from gensim.models.phrases import Phraser
from pprint import pprint
from sklearn.preprocessing import normalize
from transformers import BartTokenizer, BartForConditionalGeneration
from typing import List
import contractions
import ftfy
import gensim.models.keyedvectors as word2vec
import json
import numpy as np
import operator
import os
import pandas as pd
import re
import string
import torch
import re
CURRENCIES = {'$': 'USD', 'zł': 'PLN', '£': 'GBP', '¥': 'JPY', '฿': 'THB', '₡': 'CRC', '₦': 'NGN', '₩': 'KRW', '₪': 'ILS', '₫': 'VND', '€': 'EUR', '₱': 'PHP', '₲': 'PYG', '₴': 'UAH', '₹': 'INR'}
RE_NUMBER = re.compile('(?:^|(?<=[^\\w,.]))[+–-]?(([1-9]\\d{0,2}(,\\d{3})+(\\.\\d*)?)|([1-9]\\d{0,2}([ .]\\d{3})+(,\\d*)?)|(\\d*?[.,]\\d+)|\\d+)(?:$|(?=\\b))')
RE_URL = re.compile('((http://www\\.|https://www\\.|http://|https://)?' + '[a-z0-9]+([\\-.][a-z0-9]+)*\\.[a-z]{2,5}(:[0-9]{1,5})?(/.*)?)')
STOP_WORDS = {'a', 'an', 'and', 'are', 'as', 'at', 'be', 'but', 'by', 'for', 'if', 'in', 'into', 'is', 'it', 'no', 'not', 'of', 'on', 'or', 'such', 'that', 'the', 'their', 'then', 'there', 'these', 'they', 'this', 'to', 'was', 'will', 'with'}
import string
from typing import List
import ftfy
import contractions
def clean_tokenized_sentence(tokens: List[str], unicode_normalization='NFC', unpack_contractions=False, replace_currency_symbols=False, remove_punct=True, remove_numbers=False, lowercase=True, remove_urls=True, remove_stop_words=True) -> str:
if remove_stop_words:
tokens = [token for token in tokens if token not in STOP_WORDS]
sentence = ' '.join(tokens)
if unicode_normalization:
sentence = ftfy.fix_text(sentence, normalization=unicode_normalization)
if unpack_contractions:
sentence = contractions.fix(sentence, slang=False)
if replace_currency_symbols:
for currency_sign, currency_tok in CURRENCIES.items():
sentence = sentence.replace(currency_sign, f'{currency_tok} ')
if remove_urls:
sentence = RE_URL.sub('_URL_', sentence)
if remove_punct:
sentence = sentence.translate(str.maketrans('', '', string.punctuation))
sentence = re.sub(' +', ' ', sentence)
if remove_numbers:
sentence = RE_NUMBER.sub('_NUMBER_', sentence)
if lowercase:
sentence = sentence.lower()
return sentence
sentences_df = pd.read_csv('../input/covid19sentencesmetadata/sentences_with_metadata.csv')
bigram_model = Phraser.load('../input/covid19phrasesmodels/covid_bigram_model_v0.pkl')
trigram_model = Phraser.load('../input/covid19phrasesmodels/covid_trigram_model_v0.pkl')
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'), 'r') as f:
for line_num, line in enumerate(f):
if first_line:
dim = int(line.strip().split()[1])
word_vecs = np.zeros((num_points, dim), dtype=float)
first_line = False
continue
line = line.strip()
word = line.split()[0]
vec = word_vecs[line_num - 1]
for index, vec_val in enumerate(line.split()[1:]):
vec[index] = float(vec_val)
index_to_word.append(word)
if line_num >= num_points:
break
word_vecs = normalize(word_vecs, copy=False, return_norm=False)
from pprint import pprint
import gensim.models.keyedvectors as word2vec
fasttext_model = word2vec.KeyedVectors.load_word2vec_format(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'))
def print_most_similar(search_term):
synonyms = fasttext_model.most_similar(search_term)
def create_articles_metadata_mapping(sentences_df: pd.DataFrame) -> dict:
sentence_id_to_metadata = {}
for row_count, row in sentences_df.iterrows():
sentence_id_to_metadata[row_count] = dict(paper_id=row['paper_id'], cord_uid=row['cord_uid'], source=row['source'], url=row['url'], publish_time=row['publish_time'], authors=row['authors'], section=row['section'], sentence=row['sentence'])
return sentence_id_to_metadata
sentence_id_to_metadata = create_articles_metadata_mapping(sentences_df)
import operator
from datetime import datetime
class SearchEngine:
def __init__(self, sentence_id_to_metadata: dict, sentences_df: pd.DataFrame, bigram_model, trigram_model, fasttext_model):
self.sentence_id_to_metadata = sentence_id_to_metadata
self.cleaned_sentences = sentences_df['cleaned_sentence'].tolist()
self.bigram_model = bigram_model
self.trigram_model = trigram_model
self.fasttext_model = fasttext_model
def _get_search_terms(self, keywords, synonyms_threshold):
cleaned_terms = [clean_tokenized_sentence(keyword.split(' ')) for keyword in keywords]
cleaned_terms = [term for term in cleaned_terms if term]
terms_with_bigrams = self.bigram_model[' '.join(cleaned_terms).split(' ')]
terms_with_trigrams = self.trigram_model[terms_with_bigrams]
search_terms = [self.fasttext_model.most_similar(token) for token in terms_with_trigrams]
search_terms = [synonym[0] for synonyms in search_terms for synonym in synonyms if synonym[1] >= synonyms_threshold]
search_terms = list(terms_with_trigrams) + search_terms
return search_terms
def search(self, keywords: List[str], optional_keywords=None, top_n: int=10, synonyms_threshold=0.7, keyword_weight: float=3.0, optional_keyword_weight: float=0.5) -> List[dict]:
if optional_keywords is None:
optional_keywords = []
search_terms = self._get_search_terms(keywords, synonyms_threshold)
optional_search_terms = self._get_search_terms(optional_keywords, synonyms_threshold) if optional_keywords else []
date_today = datetime.today()
indexes = []
match_counts = []
days_diffs = []
for sentence_index, sentence in enumerate(self.cleaned_sentences):
sentence_tokens = sentence.split(' ')
sentence_tokens_set = set(sentence_tokens)
match_count = sum([keyword_weight if keyword in sentence_tokens_set else 0 for keyword in search_terms])
if match_count > 0:
indexes.append(sentence_index)
if optional_search_terms:
match_count += sum([optional_keyword_weight if keyword in sentence_tokens_set else 0 for keyword in optional_search_terms])
match_counts.append(match_count)
article_date = self.sentence_id_to_metadata[sentence_index]['publish_time']
if article_date == '2020':
article_date = '2020-01-01'
article_date = datetime.strptime(article_date, '%Y-%m-%d')
days_diff = (date_today - article_date).days
days_diffs.append(days_diff)
match_counts = [float(match_count) / sum(match_counts) for match_count in match_counts]
days_diffs = [max(days_diffs) - days_diff for days_diff in days_diffs]
days_diffs = [float(days_diff) / sum(days_diffs) for days_diff in days_diffs]
index_to_score = {}
for index, match_count, days_diff in zip(indexes, match_counts, days_diffs):
index_to_score[index] = 0.7 * match_count + 0.3 * days_diff
sorted_indexes = sorted(index_to_score.items(), key=operator.itemgetter(1), reverse=True)
sorted_indexes = [item[0] for item in sorted_indexes]
sorted_indexes = sorted_indexes[0:min(top_n, len(sorted_indexes))]
results = []
for index in sorted_indexes:
results.append(self.sentence_id_to_metadata[index])
return results
task_id = 2
import json
with open(f'../input/covid19seedsentences/{task_id}.json') as in_fp:
seed_sentences_json = json.load(in_fp)
import torch
from transformers import BartTokenizer, BartForConditionalGeneration
class BartSummarizer:
def __init__(self):
self.torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
model_name = 'bart-large-cnn'
self.tokenizer_summarize = BartTokenizer.from_pretrained(model_name)
self.model_summarize = BartForConditionalGeneration.from_pretrained(model_name)
self.model_summarize.to(self.torch_device)
self.model_summarize.eval()
def create_summary(self, text: str, repetition_penalty=1.0) -> str:
text_input_ids = self.tokenizer_summarize.batch_encode_plus([text], return_tensors='pt', max_length=1024)['input_ids'].to(self.torch_device)
summary_ids = self.model_summarize.generate(text_input_ids, num_beams=4, max_length=1024, min_length=256, no_repeat_ngram_size=4, repetition_penalty=repetition_penalty)
summary = self.tokenizer_summarize.decode(summary_ids.squeeze(), skip_special_tokens=True)
return summary
bart_summarizer = BartSummarizer()
with open(f'../input/covid19seedsentences/{task_id}_relevant_sentences.json') as in_fp:
relevant_sentences_json = json.load(in_fp)
answers_results = []
for idx, sub_task_json in enumerate(relevant_sentences_json['subTasks']):
sub_task_description = sub_task_json['sub_task_description']
best_sentences = seed_sentences_json['subTasks'][idx]['bestSentences']
relevant_sentences = sub_task_json['relevant_sentences']
relevant_sentences_texts = [result['sentence'] for result in relevant_sentences]
sub_task_summary = bart_summarizer.create_summary(' '.join(best_sentences + relevant_sentences_texts))
answers_results.append(dict(sub_task_description=sub_task_description, relevant_sentences=relevant_sentences, sub_task_summary=sub_task_summary))
from IPython.display import display, HTML
pd.set_option('display.max_colwidth', 0)
def display_summary(summary: str):
return
def display_sub_task_description(sub_task_description):
return
def display_task_name(task_name):
return
def visualize_output(sub_task_json):
"""
Prints output for each sub-task
"""
results = sub_task_json.get('relevant_sentences')
sentence_output = pd.DataFrame(sub_task_json.get('relevant_sentences'))
sentence_output.rename(columns={'sentence': 'Relevant Sentence', 'cord_id': 'CORD UID', 'publish_time': 'Publish Time', 'url': 'URL', 'source': 'Source'}, inplace=True)
def save_output(seed_sentences, sub_task_json):
"""
Saves output for each sub-task
"""
sentence_output = pd.DataFrame(sub_task_json.get('relevant_sentences'))
sentence_output.rename(columns={'sentence': 'Relevant Sentence', 'cord_id': 'CORD UID', 'publish_time': 'Publish Time', 'url': 'URL', 'source': 'Source'}, inplace=True)
return sentence_output[['cord_uid', 'Source', 'Publish Time', 'Relevant Sentence', 'URL']]
relevant_sentences = []
for idx, sub_task_json in enumerate(answers_results):
task_sentences = save_output(seed_sentences_json['subTasks'][idx]['bestSentences'], sub_task_json)
relevant_sentences.append(task_sentences)
all_relevant_sentences = pd.concat(relevant_sentences).reset_index()
all_relevant_sentences.head(1) | code |
32068402/cell_47 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from pprint import pprint
from sklearn.preprocessing import normalize
import gensim.models.keyedvectors as word2vec
import numpy as np
import os
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'), 'r') as f:
for line_num, line in enumerate(f):
if first_line:
dim = int(line.strip().split()[1])
word_vecs = np.zeros((num_points, dim), dtype=float)
first_line = False
continue
line = line.strip()
word = line.split()[0]
vec = word_vecs[line_num - 1]
for index, vec_val in enumerate(line.split()[1:]):
vec[index] = float(vec_val)
index_to_word.append(word)
if line_num >= num_points:
break
word_vecs = normalize(word_vecs, copy=False, return_norm=False)
from pprint import pprint
import gensim.models.keyedvectors as word2vec
fasttext_model = word2vec.KeyedVectors.load_word2vec_format(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'))
def print_most_similar(search_term):
synonyms = fasttext_model.most_similar(search_term)
print_most_similar('fake_news') | code |
32068402/cell_46 | [
"image_output_1.png"
] | from pprint import pprint
from sklearn.preprocessing import normalize
import gensim.models.keyedvectors as word2vec
import numpy as np
import os
fasttext_model_dir = '../input/fasttext-no-subwords-trigrams'
num_points = 400
first_line = True
index_to_word = []
with open(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'), 'r') as f:
for line_num, line in enumerate(f):
if first_line:
dim = int(line.strip().split()[1])
word_vecs = np.zeros((num_points, dim), dtype=float)
first_line = False
continue
line = line.strip()
word = line.split()[0]
vec = word_vecs[line_num - 1]
for index, vec_val in enumerate(line.split()[1:]):
vec[index] = float(vec_val)
index_to_word.append(word)
if line_num >= num_points:
break
word_vecs = normalize(word_vecs, copy=False, return_norm=False)
from pprint import pprint
import gensim.models.keyedvectors as word2vec
fasttext_model = word2vec.KeyedVectors.load_word2vec_format(os.path.join(fasttext_model_dir, 'word-vectors-100d.txt'))
def print_most_similar(search_term):
synonyms = fasttext_model.most_similar(search_term)
print_most_similar('new_coronavirus') | code |
32068402/cell_24 | [
"text_html_output_1.png"
] | import pandas as pd
sentences_df = pd.read_csv('../input/covid19sentencesmetadata/sentences_with_metadata.csv')
print(f'Sentence count: {len(sentences_df)}') | code |
32068402/cell_14 | [
"text_plain_output_1.png"
] | !pip install contractions | code |
34128236/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z]))
female_fight_counts = []
for z in year_labels:
female_fight_counts.append(len(df[(df['date'].dt.year == z) & (df['gender'] == 'FEMALE')]))
df_no_even = df[df['underdog'] != 'Even']
df_no_even = df_no_even[df_no_even['Winner'] != 'Draw']
number_of_fights = len(df_no_even)
number_of_upsets = len(df_no_even[df_no_even['Winner'] == df_no_even['underdog']])
number_of_favorites = len(df_no_even[df_no_even['Winner'] != df_no_even['underdog']])
upset_percent = number_of_upsets / number_of_fights * 100
favorite_percent = number_of_favorites / number_of_fights * 100
labels = ('Favorites', 'Underdogs')
sizes = [favorite_percent, upset_percent]
fig1, ax1 = plt.subplots(figsize=(9, 9))
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', textprops={'fontsize': 14}) | code |
34128236/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df['country'] = df['country'].str.strip()
display(df[['country']].describe())
display(df['country'].unique()) | code |
34128236/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['R_fighter', 'B_fighter']].describe() | code |
34128236/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['title_bout'].describe()) | code |
34128236/cell_23 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['Winner'].describe())
print()
print(df['Winner'].unique()) | code |
34128236/cell_33 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z]))
plt.figure(figsize=(9, 5))
plt.plot(year_labels, fight_counts)
plt.xlabel('Year', fontsize=16)
plt.ylabel('# of Fights', fontsize=16)
plt.title('Fights Per Year', fontweight='bold', fontsize=16)
plt.show() | code |
34128236/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['gender'].describe()) | code |
34128236/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df_no_even = df[df['underdog'] != 'Even']
df_no_even = df_no_even[df_no_even['Winner'] != 'Draw']
print(f'Number of fights including even fights and draws: {len(df)}')
print(f'Number of fights with even fights and draws removed: {len(df_no_even)}') | code |
34128236/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['location']].describe() | code |
34128236/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z]))
female_fight_counts = []
for z in year_labels:
female_fight_counts.append(len(df[(df['date'].dt.year == z) & (df['gender'] == 'FEMALE')]))
df_no_even = df[df['underdog'] != 'Even']
df_no_even = df_no_even[df_no_even['Winner'] != 'Draw']
number_of_fights = len(df_no_even)
number_of_upsets = len(df_no_even[df_no_even['Winner'] == df_no_even['underdog']])
number_of_favorites = len(df_no_even[df_no_even['Winner'] != df_no_even['underdog']])
#print(number_of_upsets)
#print(number_of_fights)
#print(number_of_favorites)
upset_percent = (number_of_upsets / number_of_fights) * 100
favorite_percent = (number_of_favorites / number_of_fights) * 100
#print(upset_percent)
#print(favorite_percent)
labels = 'Favorites', 'Underdogs'
sizes = [favorite_percent, upset_percent]
fig1, ax1 = plt.subplots(figsize=(9,9))
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', textprops={'fontsize': 14})
year_labels
year_fight_counts = []
year_upset_counts = []
year_upset_percent = []
for y in year_labels:
temp_fights = df_no_even[df_no_even['date'].dt.year == y]
temp_upsets = temp_fights[temp_fights['Winner'] == temp_fights['underdog']]
year_fight_counts.append(len(temp_fights))
year_upset_counts.append(len(temp_upsets))
year_upset_percent.append(len(temp_upsets) / len(temp_fights))
year_upset_percent = [x * 100 for x in year_upset_percent]
plt.figure(figsize=(9, 5))
barlist = plt.bar(year_labels, year_upset_percent)
plt.xlabel('Year', fontsize=16)
plt.ylabel('Percent of Upset Winners', fontsize=16)
plt.xticks(year_labels, rotation=90)
plt.title('Upset Percentage By Year', fontweight='bold', fontsize=16)
barlist[10].set_color('black')
barlist[3].set_color('grey') | code |
34128236/cell_49 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df['date'] = pd.to_datetime(df['date'])
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z]))
female_fight_counts = []
for z in year_labels:
female_fight_counts.append(len(df[(df['date'].dt.year == z) & (df['gender'] == 'FEMALE')]))
df_no_even = df[df['underdog'] != 'Even']
df_no_even = df_no_even[df_no_even['Winner'] != 'Draw']
number_of_fights = len(df_no_even)
number_of_upsets = len(df_no_even[df_no_even['Winner'] == df_no_even['underdog']])
number_of_favorites = len(df_no_even[df_no_even['Winner'] != df_no_even['underdog']])
#print(number_of_upsets)
#print(number_of_fights)
#print(number_of_favorites)
upset_percent = (number_of_upsets / number_of_fights) * 100
favorite_percent = (number_of_favorites / number_of_fights) * 100
#print(upset_percent)
#print(favorite_percent)
labels = 'Favorites', 'Underdogs'
sizes = [favorite_percent, upset_percent]
fig1, ax1 = plt.subplots(figsize=(9,9))
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', textprops={'fontsize': 14})
year_labels
year_fight_counts = []
year_upset_counts = []
year_upset_percent = []
for y in year_labels:
temp_fights = df_no_even[df_no_even['date'].dt.year==y]
temp_upsets = temp_fights[temp_fights['Winner'] == temp_fights['underdog']]
year_fight_counts.append(len(temp_fights))
year_upset_counts.append(len(temp_upsets))
year_upset_percent.append(len(temp_upsets)/len(temp_fights))
#print(year_fight_counts)
#print()
#print(year_upset_counts)
#print()
#print(year_upset_percent)
year_upset_percent = [x*100 for x in year_upset_percent]
plt.figure(figsize=(9,5))
barlist = plt.bar(year_labels, year_upset_percent)
plt.xlabel("Year", fontsize=16)
plt.ylabel("Percent of Upset Winners", fontsize=16)
plt.xticks(year_labels, rotation=90)
plt.title('Upset Percentage By Year', fontweight='bold', fontsize=16)
barlist[10].set_color('black')
barlist[3].set_color('grey')
temp_df = pd.DataFrame({"Percent of Underdog Winners": year_upset_percent},
index=year_labels)
fig, ax = plt.subplots(figsize=(4,8))
sns.heatmap(temp_df, annot=True, fmt=".4g", cmap='binary', ax=ax)
plt.yticks(rotation=0)
plt.title("Upset Percentage by Year", fontsize=16, fontweight='bold')
weight_class_list = ['Flyweight', 'Bantamweight', 'Featherweight', 'Lightweight', 'Welterweight', 'Middleweight', 'Light Heavyweight', 'Heavyweight', "Women's Strawweight", "Women's Flyweight", "Women's Bantamweight", "Women's Featherweight", 'Catch Weight']
wc_fight_counts = []
wc_upset_counts = []
wc_upset_percent = []
for wc in weight_class_list:
temp_fights = df_no_even[df_no_even['weight_class'] == wc]
temp_upsets = temp_fights[temp_fights['Winner'] == temp_fights['underdog']]
wc_fight_counts.append(len(temp_fights))
wc_upset_counts.append(len(temp_upsets))
wc_upset_percent.append(len(temp_upsets) / len(temp_fights))
wc_upset_percent = [x * 100 for x in wc_upset_percent]
plt.figure(figsize=(9, 5))
barlist = plt.bar(weight_class_list, wc_upset_percent)
plt.xlabel('Weight Class', fontsize=16)
plt.ylabel('Percent of Upset Winners', fontsize=16)
plt.xticks(weight_class_list, rotation=90)
plt.title('Upset Percentage By Weight Class', fontweight='bold', fontsize=16)
barlist[9].set_color('black')
barlist[11].set_color('grey') | code |
34128236/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['date']].describe() | code |
34128236/cell_38 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df['underdog'] = ''
red_underdog_mask = df['R_odds'] > df['B_odds']
blue_underdog_mask = df['B_odds'] > df['R_odds']
even_mask = df['B_odds'] == df['R_odds']
df['underdog'][red_underdog_mask] = 'Red'
df['underdog'][blue_underdog_mask] = 'Blue'
df['underdog'][even_mask] = 'Even' | code |
34128236/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df[['R_odds', 'B_odds']].describe() | code |
34128236/cell_35 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z]))
female_fight_counts = []
for z in year_labels:
female_fight_counts.append(len(df[(df['date'].dt.year == z) & (df['gender'] == 'FEMALE')]))
plt.figure(figsize=(9, 5))
plt.plot(year_labels, female_fight_counts)
plt.xlabel('Year', fontsize=16)
plt.ylabel('# of Fights', fontsize=16)
plt.title('Female Fights Per Year', fontweight='bold', fontsize=16)
plt.show() | code |
34128236/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df['date'] = pd.to_datetime(df['date'])
df = df.dropna()
year_labels = []
for z in range(2010, 2021):
year_labels.append(z)
fight_counts = []
for z in year_labels:
fight_counts.append(len(df[df['date'].dt.year == z]))
female_fight_counts = []
for z in year_labels:
female_fight_counts.append(len(df[(df['date'].dt.year == z) & (df['gender'] == 'FEMALE')]))
df_no_even = df[df['underdog'] != 'Even']
df_no_even = df_no_even[df_no_even['Winner'] != 'Draw']
number_of_fights = len(df_no_even)
number_of_upsets = len(df_no_even[df_no_even['Winner'] == df_no_even['underdog']])
number_of_favorites = len(df_no_even[df_no_even['Winner'] != df_no_even['underdog']])
#print(number_of_upsets)
#print(number_of_fights)
#print(number_of_favorites)
upset_percent = (number_of_upsets / number_of_fights) * 100
favorite_percent = (number_of_favorites / number_of_fights) * 100
#print(upset_percent)
#print(favorite_percent)
labels = 'Favorites', 'Underdogs'
sizes = [favorite_percent, upset_percent]
fig1, ax1 = plt.subplots(figsize=(9,9))
ax1.pie(sizes, labels=labels, autopct='%1.1f%%', textprops={'fontsize': 14})
year_labels
year_fight_counts = []
year_upset_counts = []
year_upset_percent = []
for y in year_labels:
temp_fights = df_no_even[df_no_even['date'].dt.year==y]
temp_upsets = temp_fights[temp_fights['Winner'] == temp_fights['underdog']]
year_fight_counts.append(len(temp_fights))
year_upset_counts.append(len(temp_upsets))
year_upset_percent.append(len(temp_upsets)/len(temp_fights))
#print(year_fight_counts)
#print()
#print(year_upset_counts)
#print()
#print(year_upset_percent)
year_upset_percent = [x*100 for x in year_upset_percent]
plt.figure(figsize=(9,5))
barlist = plt.bar(year_labels, year_upset_percent)
plt.xlabel("Year", fontsize=16)
plt.ylabel("Percent of Upset Winners", fontsize=16)
plt.xticks(year_labels, rotation=90)
plt.title('Upset Percentage By Year', fontweight='bold', fontsize=16)
barlist[10].set_color('black')
barlist[3].set_color('grey')
temp_df = pd.DataFrame({'Percent of Underdog Winners': year_upset_percent}, index=year_labels)
fig, ax = plt.subplots(figsize=(4, 8))
sns.heatmap(temp_df, annot=True, fmt='.4g', cmap='binary', ax=ax)
plt.yticks(rotation=0)
plt.title('Upset Percentage by Year', fontsize=16, fontweight='bold') | code |
34128236/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
df.info(verbose=True) | code |
34128236/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df = df.dropna()
print(df['weight_class'].describe())
print()
print(df['weight_class'].unique()) | code |
34128236/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/ufc-fights-2010-2020-with-betting-odds/data.csv')
df.info(verbose=True) | code |
130010382/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
train = pd.concat([data, original])
test = train[train['x_e_out [-]'].isnull()]
train = train[train['x_e_out [-]'].notnull()]
print('Columns:', train.info()) | code |
130010382/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
data.head() | code |
130010382/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
train = pd.concat([data, original])
test = train[train['x_e_out [-]'].isnull()]
train = train[train['x_e_out [-]'].notnull()]
test.head() | code |
130010382/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas_profiling
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)
import warnings
warnings.filterwarnings('ignore')
from IPython.display import Image
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.isotonic import IsotonicRegression
import random
import os
from copy import deepcopy
from functools import partial
from itertools import combinations
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.model_selection import StratifiedKFold, KFold, RepeatedKFold
from sklearn.metrics import roc_auc_score, accuracy_score, mean_squared_error, mean_absolute_error
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.decomposition import PCA
import seaborn as sns
from sklearn import preprocessing
from category_encoders import OneHotEncoder, OrdinalEncoder, CountEncoder
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import VotingRegressor
import optuna
import xgboost as xgb
import lightgbm as lgb
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
from catboost import CatBoost, CatBoostRegressor, CatBoostClassifier
from catboost import Pool
from h2o.automl import H2OAutoML
import warnings
warnings.filterwarnings('ignore', category=UserWarning)
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130010382/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
train = pd.concat([data, original])
test = train[train['x_e_out [-]'].isnull()]
train = train[train['x_e_out [-]'].notnull()]
train.head() | code |
130010382/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/playground-series-s3e15/data.csv')
sample = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')
original = pd.read_csv('/kaggle/input/predicting-heat-flux/Data_CHF_Zhao_2020_ATE.csv')
sample.head() | code |
49127503/cell_4 | [
"text_plain_output_1.png"
] | a, b, c = (10, 21, 0)
for i in range(10):
print(a)
c = a + b
a = b
b = c | code |
49127503/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | bil1 = int(input('Masukan Angka :'))
hasil = bil1 * bil1 * bil1
print('program konversi harga emas ke rupiah')
bil1 = int(input('masukan berat emas:'))
print('%d' % bil1)
hasil = bil1 * 10159000
print('harga emas %d gram adalah:Rp,%d' % (bil1, hasil)) | code |
49127503/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | bil1 = int(input('Masukan Angka :'))
hasil = bil1 * bil1 * bil1
print('Pangkat 3 dari bilangan %d adalah : %d' % (bil1, hasil)) | code |
49127503/cell_7 | [
"text_plain_output_1.png"
] | a, b, c = (10, 21, 0)
for i in range(10):
c = a + b
a = b
b = c
for a in range(50, 64, 4):
print(a, end=',')
for a in range(64, 74, 4):
print(a, end=',')
for a in range(74, 84, 4):
print(a, end=',')
for a in range(84, 94, 4):
print(a, end=',')
for a in range(94, 103, 4):
print(a, end=',') | code |
49127503/cell_5 | [
"text_plain_output_1.png"
] | for i in range(10, 0, -1):
print(' ' * (i - 1) + '*' * (11 - i) + '*' * (10 - i))
for i in range(10, 0, -1):
print(' ' * (10 - i) + '*' * i + '*' * (i - 1)) | code |
50231823/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target) | code |
50231823/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target) | code |
50231823/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.head() | code |
50231823/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape | code |
50231823/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
df['target'].value_counts() | code |
50231823/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.cp, df.target)
pd.crosstab(df.cp, df.target).plot(kind='bar', rot=0, xlabel='Chest Pain', ylabel='Frequency', title='Frequency Graph between the Chest Pain and Target') | code |
50231823/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.shape
pd.crosstab(df.sex, df.target)
pd.crosstab(df.sex, df.target).plot(kind='bar', rot=0, ylabel='Frequency', xlabel='Sex', title='Frequency graph between the Sex and Target') | code |
50231823/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heart-disease-uci/heart.csv')
df.describe() | code |
16150211/cell_42 | [
"text_html_output_1.png"
] | from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['start_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['depart_time_hr_sin'] = hr_sin
df['depart_time_hr_cos'] = hr_cos
df['depart_time_min_sin'] = min_sin
df['depart_time_min_cos'] = min_cos
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['end_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['arrival_time_hr_sin'] = hr_sin
df['arrival_time_hr_cos'] = hr_cos
df['arrival_time_min_sin'] = min_sin
df['arrival_time_min_cos'] = min_cos
df.drop(['start_date'], axis=1, inplace=True)
df.drop(['end_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot=True, cmap = "Greens", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
data = df.values
Y = data[:, 3]
X = np.delete(data, 3, 1)
x_train = X[:2223708]
y_train = Y[:2223708]
x_validation = X[2223708:2246398]
y_validation = Y[2223708:2246398]
x_test = X[2246398:]
y_test = Y[2246398:]
input_layer = Input((X.shape[1],))
y = Dense(64, kernel_initializer='he_normal', activation='tanh')(input_layer)
y = Dense(8, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='tanh')(y)
model = Model(inputs=input_layer, outputs=y)
model.compile(Nadam(), loss='mse')
model.summary() | code |
16150211/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f, ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap='Blues', linewidths=0.5, fmt='.2f', ax=ax)
plt.show() | code |
16150211/cell_33 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
df.drop(['start_date'], axis=1, inplace=True)
df.drop(['end_date'], axis=1, inplace=True)
f, ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot=True, cmap='Greens', linewidths=0.5, fmt='.2f', ax=ax)
plt.show() | code |
16150211/cell_44 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['start_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['depart_time_hr_sin'] = hr_sin
df['depart_time_hr_cos'] = hr_cos
df['depart_time_min_sin'] = min_sin
df['depart_time_min_cos'] = min_cos
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['end_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['arrival_time_hr_sin'] = hr_sin
df['arrival_time_hr_cos'] = hr_cos
df['arrival_time_min_sin'] = min_sin
df['arrival_time_min_cos'] = min_cos
df.drop(['start_date'], axis=1, inplace=True)
df.drop(['end_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot=True, cmap = "Greens", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
data = df.values
Y = data[:, 3]
X = np.delete(data, 3, 1)
x_train = X[:2223708]
y_train = Y[:2223708]
x_validation = X[2223708:2246398]
y_validation = Y[2223708:2246398]
x_test = X[2246398:]
y_test = Y[2246398:]
input_layer = Input((X.shape[1],))
y = Dense(64, kernel_initializer='he_normal', activation='tanh')(input_layer)
y = Dense(8, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='tanh')(y)
model = Model(inputs=input_layer, outputs=y)
model.compile(Nadam(), loss='mse')
model.summary()
history = model.fit(x_train, y_train, validation_data=(x_validation, y_validation), epochs=100, batch_size=2048, callbacks=[ModelCheckpoint('best_model.hdf5', monitor='val_loss', mode='min')]) | code |
16150211/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import math
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['start_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['depart_time_hr_sin'] = hr_sin
df['depart_time_hr_cos'] = hr_cos
df['depart_time_min_sin'] = min_sin
df['depart_time_min_cos'] = min_cos
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['end_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['arrival_time_hr_sin'] = hr_sin
df['arrival_time_hr_cos'] = hr_cos
df['arrival_time_min_sin'] = min_sin
df['arrival_time_min_cos'] = min_cos | code |
16150211/cell_48 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
from sklearn.preprocessing import MinMaxScaler
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
k = df['train_type'].unique()
l = [x for x in range(len(k))]
df['train_type'].replace(k, l, inplace=True)
k = df['train_class'].unique()
l = [x for x in range(len(k))]
df['train_class'].replace(k, l, inplace=True)
k = df['fare'].unique()
l = [x for x in range(len(k))]
df['fare'].replace(k, l, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['start_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['depart_time_hr_sin'] = hr_sin
df['depart_time_hr_cos'] = hr_cos
df['depart_time_min_sin'] = min_sin
df['depart_time_min_cos'] = min_cos
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['end_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['arrival_time_hr_sin'] = hr_sin
df['arrival_time_hr_cos'] = hr_cos
df['arrival_time_min_sin'] = min_sin
df['arrival_time_min_cos'] = min_cos
df.drop(['start_date'], axis=1, inplace=True)
df.drop(['end_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot=True, cmap = "Greens", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
places_sc = MinMaxScaler(copy=False)
train_type_sc = MinMaxScaler(copy=False)
train_class_sc = MinMaxScaler(copy=False)
fare_sc = MinMaxScaler(copy=False)
weekday_sc = MinMaxScaler(copy=False)
duration_sc = MinMaxScaler(copy=False)
price_sc = MinMaxScaler(copy=False)
df['origin'] = places_sc.fit_transform(df['origin'].values.reshape(-1, 1))
df['destination'] = places_sc.fit_transform(df['destination'].values.reshape(-1, 1))
df['train_type'] = train_type_sc.fit_transform(df['train_type'].values.reshape(-1, 1))
df['train_class'] = train_class_sc.fit_transform(df['train_class'].values.reshape(-1, 1))
df['fare'] = fare_sc.fit_transform(df['fare'].values.reshape(-1, 1))
df['start_weekday'] = weekday_sc.fit_transform(df['start_weekday'].values.reshape(-1, 1))
df['end_weekday'] = weekday_sc.fit_transform(df['end_weekday'].values.reshape(-1, 1))
df['duration'] = duration_sc.fit_transform(df['duration'].values.reshape(-1, 1))
df['price'] = price_sc.fit_transform(df['price'].values.reshape(-1, 1))
data = df.values
Y = data[:, 3]
X = np.delete(data, 3, 1)
x_train = X[:2223708]
y_train = Y[:2223708]
x_validation = X[2223708:2246398]
y_validation = Y[2223708:2246398]
x_test = X[2246398:]
y_test = Y[2246398:]
input_layer = Input((X.shape[1],))
y = Dense(64, kernel_initializer='he_normal', activation='tanh')(input_layer)
y = Dense(8, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='tanh')(y)
model = Model(inputs=input_layer, outputs=y)
model.compile(Nadam(), loss='mse')
model.summary()
history = model.fit(x_train, y_train, validation_data=(x_validation, y_validation), epochs=100, batch_size=2048, callbacks=[ModelCheckpoint('best_model.hdf5', monitor='val_loss', mode='min')])
model.load_weights('best_model.hdf5')
scores = model.evaluate(x_test, y_test)
print('Test Set RMSE(before scaling ):', scores)
pred = model.predict(x_test)
y_test = y_test.reshape(22692, 1)
k = y_test - pred
k = price_sc.inverse_transform(k)
rmse = np.sqrt(np.mean(np.square(k)))
print('Test Set RMSE(after scaling) :', rmse) | code |
16150211/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df['origin'].value_counts().plot(kind='bar') | code |
16150211/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
k = df['train_type'].unique()
l = [x for x in range(len(k))]
df['train_type'].replace(k, l, inplace=True)
k = df['train_class'].unique()
l = [x for x in range(len(k))]
df['train_class'].replace(k, l, inplace=True)
df['fare'].value_counts().plot(kind='bar')
k = df['fare'].unique()
l = [x for x in range(len(k))]
print('Numbers used to encode different fare classes:', l)
df['fare'].replace(k, l, inplace=True) | code |
16150211/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import pickle
import datetime
import math
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from keras.layers import Input, Dense
from keras.models import Model
from keras.optimizers import Nadam
from keras.callbacks import ModelCheckpoint | code |
16150211/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
k = df['train_type'].unique()
l = [x for x in range(len(k))]
df['train_type'].replace(k, l, inplace=True)
df['train_class'].value_counts().plot(kind='bar')
k = df['train_class'].unique()
l = [x for x in range(len(k))]
print('Numbers used to encode different train classes:', l)
df['train_class'].replace(k, l, inplace=True) | code |
16150211/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df.head() | code |
16150211/cell_38 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
df.drop(['start_date'], axis=1, inplace=True)
df.drop(['end_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot=True, cmap = "Greens", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
df.head() | code |
16150211/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.head() | code |
16150211/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df['train_type'].value_counts().plot(kind='bar')
k = df['train_type'].unique()
l = [x for x in range(len(k))]
print('Numbers used to encode different train types', l)
df['train_type'].replace(k, l, inplace=True) | code |
16150211/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
df.drop(['start_date'], axis=1, inplace=True)
df.drop(['end_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot=True, cmap = "Greens", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
df.head() | code |
16150211/cell_46 | [
"text_plain_output_1.png"
] | from keras.callbacks import ModelCheckpoint
from keras.layers import Input,Dense
from keras.models import Model
from keras.optimizers import Nadam
import math
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(6, 6))
sns.heatmap(df.corr(), annot=True, cmap = "Blues", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['start_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['depart_time_hr_sin'] = hr_sin
df['depart_time_hr_cos'] = hr_cos
df['depart_time_min_sin'] = min_sin
df['depart_time_min_cos'] = min_cos
hr_cos = []
hr_sin = []
min_cos = []
min_sin = []
data = df['end_date'].values
for i in range(len(data)):
time_obj = dt.datetime.strptime(data[i], '%Y-%m-%d %H:%M:%S')
hr = time_obj.hour
minute = time_obj.minute
sample_hr_sin = math.sin(hr * (2.0 * math.pi / 24))
sample_hr_cos = math.cos(hr * (2.0 * math.pi / 24))
sample_min_sin = math.sin(minute * (2.0 * math.pi / 60))
sample_min_cos = math.cos(minute * (2.0 * math.pi / 60))
hr_cos.append(sample_hr_cos)
hr_sin.append(sample_hr_sin)
min_cos.append(sample_min_cos)
min_sin.append(sample_min_sin)
df['arrival_time_hr_sin'] = hr_sin
df['arrival_time_hr_cos'] = hr_cos
df['arrival_time_min_sin'] = min_sin
df['arrival_time_min_cos'] = min_cos
df.drop(['start_date'], axis=1, inplace=True)
df.drop(['end_date'], axis=1, inplace=True)
f,ax = plt.subplots(figsize=(20, 20))
sns.heatmap(df.corr(), annot=True, cmap = "Greens", linewidths=.5, fmt= '.2f',ax = ax)
plt.show()
data = df.values
Y = data[:, 3]
X = np.delete(data, 3, 1)
x_train = X[:2223708]
y_train = Y[:2223708]
x_validation = X[2223708:2246398]
y_validation = Y[2223708:2246398]
x_test = X[2246398:]
y_test = Y[2246398:]
input_layer = Input((X.shape[1],))
y = Dense(64, kernel_initializer='he_normal', activation='tanh')(input_layer)
y = Dense(8, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='sigmoid')(y)
y = Dense(1, kernel_initializer='he_normal', activation='tanh')(y)
model = Model(inputs=input_layer, outputs=y)
model.compile(Nadam(), loss='mse')
model.summary()
history = model.fit(x_train, y_train, validation_data=(x_validation, y_validation), epochs=100, batch_size=2048, callbacks=[ModelCheckpoint('best_model.hdf5', monitor='val_loss', mode='min')])
plt.plot(history.history['loss'], label='train')
plt.plot(history.history['val_loss'], label='test')
plt.ylabel('loss')
plt.xlabel('epochs')
plt.legend()
plt.show() | code |
16150211/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum()
df.dropna(inplace=True)
df.drop(['Unnamed: 0'], axis=1, inplace=True)
df.drop(['insert_date'], axis=1, inplace=True)
df['destination'].value_counts().plot(kind='bar') | code |
16150211/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/renfe.csv')
df.isna().sum() | code |
90127400/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train = df_train.drop('language', axis=1)
df_test = df_test.drop('language', axis=1)
df_test.head() | code |
90127400/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train.head() | code |
90127400/cell_6 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train.info() | code |
90127400/cell_2 | [
"text_html_output_1.png"
] | !nvidia-smi | code |
90127400/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90127400/cell_7 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
print(df_test) | code |
90127400/cell_8 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train.describe(include='all') | code |
90127400/cell_14 | [
"text_plain_output_1.png"
] | import cudf as pd
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train = df_train.drop('language', axis=1)
df_test = df_test.drop('language', axis=1)
sns.countplot(x='lang_abv', data=df_train) | code |
90127400/cell_10 | [
"text_plain_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_test.describe(include='all') | code |
90127400/cell_12 | [
"text_plain_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
df_train = df_train.drop('id', axis=1)
df_train = df_train.drop('language', axis=1)
df_test = df_test.drop('language', axis=1)
df_train.head() | code |
90127400/cell_5 | [
"text_html_output_1.png"
] | import cudf as pd
df_train = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/train.csv')
df_test = pd.read_csv('/kaggle/input/contradictory-my-dear-watson/test.csv')
print(df_train) | code |
1009478/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.describe() | code |
1009478/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.hist(train_data['Pclass'], color='lightblue')
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.xlabel('Pclass')
plt.ylabel('Count')
plt.grid(True)
plt.tight_layout() | code |
1009478/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009478/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.head() | code |
128038775/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
data.head() | code |
128038775/cell_26 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
MODEL_PATH = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'
detector = hub.load(MODEL_PATH)
def detect_objects(path, model):
"""Извлекает изображение из файла, добавляет новую ось и применяет модель на объект.
"""
image_tensor = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)[tf.newaxis, ...]
return model(image_tensor)
def count_persons(path, model, threshold=0.0):
"""Считает количество людей на изображении.
"""
results = detect_objects(path, model)
return (results['detection_classes'].numpy()[0] == 1)[np.where(results['detection_scores'].numpy()[0] > threshold)].sum()
def draw_boxes(image_path, data, threshold=0.0):
"""Возвращает изображения с прямоугольниками поверх каждого обнаруженного человека.
"""
image = PIL.Image.open(image_path)
draw = Draw(image)
im_width, im_height = image.size
boxes = data['detection_boxes'].numpy()[0]
classes = data['detection_classes'].numpy()[0]
scores = data['detection_scores'].numpy()[0]
for i in range(int(data['num_detections'][0])):
if classes[i] == 1 and scores[i] > threshold:
ymin, xmin, ymax, xmax = boxes[i]
left, right, top, bottom = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=4, fill='red')
return image
example_path = '../input/crowd-counting/frames/frames/seq_000010.jpg'
results = detect_objects(example_path, detector)
draw_bboxes(example_path, results, threshold=0.5) | code |
128038775/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
data.head() | code |
128038775/cell_32 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
MODEL_PATH = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'
detector = hub.load(MODEL_PATH)
def detect_objects(path, model):
"""Извлекает изображение из файла, добавляет новую ось и применяет модель на объект.
"""
image_tensor = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)[tf.newaxis, ...]
return model(image_tensor)
def count_persons(path, model, threshold=0.0):
"""Считает количество людей на изображении.
"""
results = detect_objects(path, model)
return (results['detection_classes'].numpy()[0] == 1)[np.where(results['detection_scores'].numpy()[0] > threshold)].sum()
def draw_boxes(image_path, data, threshold=0.0):
"""Возвращает изображения с прямоугольниками поверх каждого обнаруженного человека.
"""
image = PIL.Image.open(image_path)
draw = Draw(image)
im_width, im_height = image.size
boxes = data['detection_boxes'].numpy()[0]
classes = data['detection_classes'].numpy()[0]
scores = data['detection_scores'].numpy()[0]
for i in range(int(data['num_detections'][0])):
if classes[i] == 1 and scores[i] > threshold:
ymin, xmin, ymax, xmax = boxes[i]
left, right, top, bottom = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=4, fill='red')
return image
example_path = '../input/crowd-counting/frames/frames/seq_000010.jpg'
results = detect_objects(example_path, detector)
example_path = data.loc[data['count'] == data['count'].min(), 'path'].iloc[0]
results = detect_objects(example_path, detector)
example_path = data.loc[data['count'] == data['count'].max(), 'path'].iloc[0]
results = detect_objects(example_path, detector)
draw_bboxes(example_path, results, threshold=0.25) | code |
128038775/cell_28 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
MODEL_PATH = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'
detector = hub.load(MODEL_PATH)
def detect_objects(path, model):
"""Извлекает изображение из файла, добавляет новую ось и применяет модель на объект.
"""
image_tensor = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)[tf.newaxis, ...]
return model(image_tensor)
def count_persons(path, model, threshold=0.0):
"""Считает количество людей на изображении.
"""
results = detect_objects(path, model)
return (results['detection_classes'].numpy()[0] == 1)[np.where(results['detection_scores'].numpy()[0] > threshold)].sum()
def draw_boxes(image_path, data, threshold=0.0):
"""Возвращает изображения с прямоугольниками поверх каждого обнаруженного человека.
"""
image = PIL.Image.open(image_path)
draw = Draw(image)
im_width, im_height = image.size
boxes = data['detection_boxes'].numpy()[0]
classes = data['detection_classes'].numpy()[0]
scores = data['detection_scores'].numpy()[0]
for i in range(int(data['num_detections'][0])):
if classes[i] == 1 and scores[i] > threshold:
ymin, xmin, ymax, xmax = boxes[i]
left, right, top, bottom = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=4, fill='red')
return image
example_path = '../input/crowd-counting/frames/frames/seq_000010.jpg'
results = detect_objects(example_path, detector)
draw_bboxes(example_path, results, threshold=0.25) | code |
128038775/cell_35 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
from tqdm import tqdm
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import time
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
MODEL_PATH = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'
detector = hub.load(MODEL_PATH)
def detect_objects(path, model):
"""Извлекает изображение из файла, добавляет новую ось и применяет модель на объект.
"""
image_tensor = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)[tf.newaxis, ...]
return model(image_tensor)
def count_persons(path, model, threshold=0.0):
"""Считает количество людей на изображении.
"""
results = detect_objects(path, model)
return (results['detection_classes'].numpy()[0] == 1)[np.where(results['detection_scores'].numpy()[0] > threshold)].sum()
def draw_boxes(image_path, data, threshold=0.0):
"""Возвращает изображения с прямоугольниками поверх каждого обнаруженного человека.
"""
image = PIL.Image.open(image_path)
draw = Draw(image)
im_width, im_height = image.size
boxes = data['detection_boxes'].numpy()[0]
classes = data['detection_classes'].numpy()[0]
scores = data['detection_scores'].numpy()[0]
for i in range(int(data['num_detections'][0])):
if classes[i] == 1 and scores[i] > threshold:
ymin, xmin, ymax, xmax = boxes[i]
left, right, top, bottom = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=4, fill='red')
return image
example_path = '../input/crowd-counting/frames/frames/seq_000010.jpg'
results = detect_objects(example_path, detector)
example_path = data.loc[data['count'] == data['count'].min(), 'path'].iloc[0]
results = detect_objects(example_path, detector)
example_path = data.loc[data['count'] == data['count'].max(), 'path'].iloc[0]
results = detect_objects(example_path, detector)
sample = data.sample(frac=0.1)
start = time.perf_counter()
objects = []
with concurrent.futures.ThreadPoolExecutor() as executor:
results = [executor.submit(count_persons, path, detector, 0.25) for path in sample['path']]
for f in tqdm(concurrent.futures.as_completed(results)):
objects.append(f.result())
finish = time.perf_counter()
print(f'Finished in {round(finish - start, 2)} second(s).') | code |
128038775/cell_31 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
MODEL_PATH = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'
detector = hub.load(MODEL_PATH)
def detect_objects(path, model):
"""Извлекает изображение из файла, добавляет новую ось и применяет модель на объект.
"""
image_tensor = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)[tf.newaxis, ...]
return model(image_tensor)
def count_persons(path, model, threshold=0.0):
"""Считает количество людей на изображении.
"""
results = detect_objects(path, model)
return (results['detection_classes'].numpy()[0] == 1)[np.where(results['detection_scores'].numpy()[0] > threshold)].sum()
def draw_boxes(image_path, data, threshold=0.0):
"""Возвращает изображения с прямоугольниками поверх каждого обнаруженного человека.
"""
image = PIL.Image.open(image_path)
draw = Draw(image)
im_width, im_height = image.size
boxes = data['detection_boxes'].numpy()[0]
classes = data['detection_classes'].numpy()[0]
scores = data['detection_scores'].numpy()[0]
for i in range(int(data['num_detections'][0])):
if classes[i] == 1 and scores[i] > threshold:
ymin, xmin, ymax, xmax = boxes[i]
left, right, top, bottom = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=4, fill='red')
return image
example_path = '../input/crowd-counting/frames/frames/seq_000010.jpg'
results = detect_objects(example_path, detector)
example_path = data.loc[data['count'] == data['count'].min(), 'path'].iloc[0]
results = detect_objects(example_path, detector)
draw_bboxes(example_path, results, threshold=0.25) | code |
128038775/cell_24 | [
"image_output_1.png"
] | from PIL.ImageDraw import Draw
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
MODEL_PATH = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'
detector = hub.load(MODEL_PATH)
def detect_objects(path, model):
"""Извлекает изображение из файла, добавляет новую ось и применяет модель на объект.
"""
image_tensor = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)[tf.newaxis, ...]
return model(image_tensor)
def count_persons(path, model, threshold=0.0):
"""Считает количество людей на изображении.
"""
results = detect_objects(path, model)
return (results['detection_classes'].numpy()[0] == 1)[np.where(results['detection_scores'].numpy()[0] > threshold)].sum()
def draw_boxes(image_path, data, threshold=0.0):
"""Возвращает изображения с прямоугольниками поверх каждого обнаруженного человека.
"""
image = PIL.Image.open(image_path)
draw = Draw(image)
im_width, im_height = image.size
boxes = data['detection_boxes'].numpy()[0]
classes = data['detection_classes'].numpy()[0]
scores = data['detection_scores'].numpy()[0]
for i in range(int(data['num_detections'][0])):
if classes[i] == 1 and scores[i] > threshold:
ymin, xmin, ymax, xmax = boxes[i]
left, right, top, bottom = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=4, fill='red')
return image
example_path = '../input/crowd-counting/frames/frames/seq_000010.jpg'
results = detect_objects(example_path, detector)
draw_bboxes(example_path, results) | code |
128038775/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
plt.hist(data['count'], bins=20)
plt.axvline(data.describe().loc['mean', 'count'], label='Mean value', color='green')
plt.legend()
plt.xlabel('Number of people')
plt.ylabel('Frequency')
plt.title('Target Values')
plt.show() | code |
128038775/cell_37 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL.ImageDraw import Draw
from tqdm import tqdm
import PIL
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_hub as hub
import time
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
MODEL_PATH = 'https://tfhub.dev/tensorflow/efficientdet/d0/1'
detector = hub.load(MODEL_PATH)
def detect_objects(path, model):
"""Извлекает изображение из файла, добавляет новую ось и применяет модель на объект.
"""
image_tensor = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)[tf.newaxis, ...]
return model(image_tensor)
def count_persons(path, model, threshold=0.0):
"""Считает количество людей на изображении.
"""
results = detect_objects(path, model)
return (results['detection_classes'].numpy()[0] == 1)[np.where(results['detection_scores'].numpy()[0] > threshold)].sum()
def draw_boxes(image_path, data, threshold=0.0):
"""Возвращает изображения с прямоугольниками поверх каждого обнаруженного человека.
"""
image = PIL.Image.open(image_path)
draw = Draw(image)
im_width, im_height = image.size
boxes = data['detection_boxes'].numpy()[0]
classes = data['detection_classes'].numpy()[0]
scores = data['detection_scores'].numpy()[0]
for i in range(int(data['num_detections'][0])):
if classes[i] == 1 and scores[i] > threshold:
ymin, xmin, ymax, xmax = boxes[i]
left, right, top, bottom = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=4, fill='red')
return image
example_path = '../input/crowd-counting/frames/frames/seq_000010.jpg'
results = detect_objects(example_path, detector)
example_path = data.loc[data['count'] == data['count'].min(), 'path'].iloc[0]
results = detect_objects(example_path, detector)
example_path = data.loc[data['count'] == data['count'].max(), 'path'].iloc[0]
results = detect_objects(example_path, detector)
sample = data.sample(frac=0.1)
start = time.perf_counter()
objects = []
with concurrent.futures.ThreadPoolExecutor() as executor:
results = [executor.submit(count_persons, path, detector, 0.25) for path in sample['path']]
for f in tqdm(concurrent.futures.as_completed(results)):
objects.append(f.result())
finish = time.perf_counter()
sample['prediction'] = objects
sample.head(10) | code |
128038775/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (12, 8)
plt.rcParams.update({'font.size': 14})
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.options.display.float_format = '{:.4f}'.format
META_FILE = '../input/crowd-counting/labels.csv'
data = pd.read_csv(META_FILE)
def reconstruct_path(image_id):
"""Превращает номерной ID изображения в относительный путь.
"""
image_id = str(image_id).rjust(6, '0')
return f'../input/crowd-counting/frames/frames/seq_{image_id}.jpg'
data['path'] = data['id'].apply(reconstruct_path)
data.describe() | code |
2012216/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
data_df = pd.read_csv('../input/mushrooms.csv')
data_df.info() | code |
2012216/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(columns):
if data_df[c].nunique() == 1:
single_val_c[c] = data_df[c].unique()[0]
continue
gb = data_df.groupby(c)
m = gb['y'].mean()
s = gb.size()
s = s.values / s.sum()
df = pd.DataFrame(columns=['col', 'val', 'mean_y', 'persent'], index=range(len(m)))
df['col'] = c
df['val'] = m.index.values
df['mean_y'] = m.values
df['persent'] = s
stats_df.append(df)
stats_df = pd.concat(stats_df, axis=0)
for c in single_val_c.keys():
print('The column %s only has one unique value with %r' % (c, single_val_c[c])) | code |
2012216/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(columns):
if data_df[c].nunique() == 1:
single_val_c[c] = data_df[c].unique()[0]
continue
gb = data_df.groupby(c)
m = gb['y'].mean()
s = gb.size()
s = s.values / s.sum()
df = pd.DataFrame(columns=['col', 'val', 'mean_y', 'persent'], index=range(len(m)))
df['col'] = c
df['val'] = m.index.values
df['mean_y'] = m.values
df['persent'] = s
stats_df.append(df)
stats_df = pd.concat(stats_df, axis=0)
stats_df.head() | code |
2012216/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
np.set_printoptions(suppress=True, linewidth=300)
pd.options.display.float_format = lambda x: '%0.6f' % x
print(check_output(['ls', '../input']).decode('utf-8')) | code |
2012216/cell_5 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.png",
"image_output_20.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_15.png",
"image_output_9.png",
"image_output_19.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
data_df = pd.read_csv('../input/mushrooms.csv')
data_df['y'] = data_df['class'].map({'p': 1, 'e': 0})
columns = [c for c in data_df.columns if not c in ('class', 'y')]
stats_df = []
single_val_c = {}
for i, c in enumerate(columns):
if data_df[c].nunique() == 1:
single_val_c[c] = data_df[c].unique()[0]
continue
gb = data_df.groupby(c)
m = gb['y'].mean()
s = gb.size()
s = s.values / s.sum()
df = pd.DataFrame(columns=['col', 'val', 'mean_y', 'persent'], index=range(len(m)))
df['col'] = c
df['val'] = m.index.values
df['mean_y'] = m.values
df['persent'] = s
stats_df.append(df)
sns.barplot(x=m.index, y=m)
plt.show()
stats_df = pd.concat(stats_df, axis=0) | code |
89136278/cell_6 | [
"image_output_1.png"
] | !dir | code |
89136278/cell_18 | [
"text_plain_output_1.png"
] | import torch
import torchvision.models as models
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
cnn = models.vgg19(pretrained=True).features.to(device).eval() | code |
89136278/cell_28 | [
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.models as models
import torchvision.transforms as transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
imsize = (512, 220) if torch.cuda.is_available() else (128, 220)
loader = transforms.Compose([transforms.Resize(imsize), transforms.ToTensor()])
def image_loader(image_name):
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
style_img = image_loader('./style.jpg')
content_img = image_loader('./content.jpg')
assert style_img.size() == content_img.size(), 'we need to import style and content images of the same size'
unloader = transforms.ToPILImage()
plt.ion()
def imshow(tensor, title=None):
image = tensor.cpu().clone()
image = image.squeeze(0)
image = unloader(image)
plt.pause(0.001)
class ContentLoss(nn.Module):
def __init__(self, target):
super(ContentLoss, self).__init__()
self.target = target.detach()
def forward(self, input):
self.loss = F.mse_loss(input, self.target)
return input
def gram_matrix(input):
a, b, c, d = input.size()
features = input.view(a * b, c * d)
G = torch.mm(features, features.t())
return G.div(a * b * c * d)
class StyleLoss(nn.Module):
def __init__(self, target_feature):
super(StyleLoss, self).__init__()
self.target = gram_matrix(target_feature).detach()
def forward(self, input):
G = gram_matrix(input)
self.loss = F.mse_loss(G, self.target)
return input
cnn = models.vgg19(pretrained=True).features.to(device).eval()
cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
class Normalization(nn.Module):
def __init__(self, mean, std):
super(Normalization, self).__init__()
self.mean = torch.tensor(mean).view(-1, 1, 1)
self.std = torch.tensor(std).view(-1, 1, 1)
def forward(self, img):
return (img - self.mean) / self.std
content_layers_default = ['conv_4']
style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
def get_style_model_and_losses(cnn, normalization_mean, normalization_std, style_img, content_img, content_layers=content_layers_default, style_layers=style_layers_default):
normalization = Normalization(normalization_mean, normalization_std).to(device)
content_losses = []
style_losses = []
model = nn.Sequential(normalization)
i = 0
for layer in cnn.children():
if isinstance(layer, nn.Conv2d):
i += 1
name = 'conv_{}'.format(i)
elif isinstance(layer, nn.ReLU):
name = 'relu_{}'.format(i)
layer = nn.ReLU(inplace=False)
elif isinstance(layer, nn.MaxPool2d):
name = 'pool_{}'.format(i)
elif isinstance(layer, nn.BatchNorm2d):
name = 'bn_{}'.format(i)
else:
raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
model.add_module(name, layer)
if name in content_layers:
target = model(content_img).detach()
content_loss = ContentLoss(target)
model.add_module('content_loss_{}'.format(i), content_loss)
content_losses.append(content_loss)
if name in style_layers:
target_feature = model(style_img).detach()
style_loss = StyleLoss(target_feature)
model.add_module('style_loss_{}'.format(i), style_loss)
style_losses.append(style_loss)
for i in range(len(model) - 1, -1, -1):
if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
break
model = model[:i + 1]
return (model, style_losses, content_losses)
input_img = content_img.clone()
def get_input_optimizer(input_img):
optimizer = optim.LBFGS([input_img])
return optimizer
def run_style_transfer(cnn, normalization_mean, normalization_std, content_img, style_img, input_img, num_steps=300, style_weight=1000000, content_weight=1):
"""Run the style transfer."""
model, style_losses, content_losses = get_style_model_and_losses(cnn, normalization_mean, normalization_std, style_img, content_img)
input_img.requires_grad_(True)
model.requires_grad_(False)
optimizer = get_input_optimizer(input_img)
run = [0]
while run[0] <= num_steps:
def closure():
with torch.no_grad():
input_img.clamp_(0, 1)
optimizer.zero_grad()
model(input_img)
style_score = 0
content_score = 0
for sl in style_losses:
style_score += sl.loss
for cl in content_losses:
content_score += cl.loss
style_score *= style_weight
content_score *= content_weight
loss = style_score + content_score
loss.backward()
run[0] += 1
return style_score + content_score
optimizer.step(closure)
with torch.no_grad():
input_img.clamp_(0, 1)
return input_img
output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std, content_img, style_img, input_img)
plt.figure()
imshow(output, title='Output Image')
plt.ioff()
plt.show() | code |
89136278/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
import torch
import torchvision.transforms as transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
imsize = (512, 220) if torch.cuda.is_available() else (128, 220)
loader = transforms.Compose([transforms.Resize(imsize), transforms.ToTensor()])
def image_loader(image_name):
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
style_img = image_loader('./style.jpg')
content_img = image_loader('./content.jpg')
print(style_img.size(), content_img.size())
assert style_img.size() == content_img.size(), 'we need to import style and content images of the same size' | code |
89136278/cell_22 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import torch
import torchvision.transforms as transforms
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
imsize = (512, 220) if torch.cuda.is_available() else (128, 220)
loader = transforms.Compose([transforms.Resize(imsize), transforms.ToTensor()])
def image_loader(image_name):
image = Image.open(image_name)
image = loader(image).unsqueeze(0)
return image.to(device, torch.float)
style_img = image_loader('./style.jpg')
content_img = image_loader('./content.jpg')
assert style_img.size() == content_img.size(), 'we need to import style and content images of the same size'
unloader = transforms.ToPILImage()
plt.ion()
def imshow(tensor, title=None):
image = tensor.cpu().clone()
image = image.squeeze(0)
image = unloader(image)
plt.pause(0.001)
input_img = content_img.clone()
plt.figure()
imshow(input_img, title='Input Image') | code |
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