Vishnu Nandakumar Vishnunkumar
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Vishnunkumar
/ main_es.py
Created
November 27, 2020 07:26
Time series forecasting using Exponential smoothing
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| from statsmodels.tsa.api import ExponentialSmoothing seasonal_decompose | |
| import statsmodels | |
| import pandas as pd | |
| import numpy as np | |
| import datetime | |
| from sklearn import metrics | |
| def preprocess(df): | |
| """ | |
| Preprocess the dataframe to required timeseries format |
Vishnunkumar
/ rolling_forecast.py
Created
November 27, 2020 07:37
Rolling training and forecast function
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| def rolling_forecast_es(df, df_test, p): | |
| """ | |
| Does rolling training and forecast for one week at a time | |
| """ | |
| df_test['preds'] = 0 | |
| for k in range(0, int(len(df_test)/p)): | |
| model = ExponentialSmoothing(np.asarray(df['Page.Loads'].iloc[:ix2 + (k)*p]),seasonal_periods=365, seasonal='add', trend='add') | |
| model_fit = model.fit() | |
| preds = model_fit.forecast(p) |
Vishnunkumar
/ one time forecast.py
Created
November 27, 2020 07:38
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| df_es = df_test.copy() | |
| df_es['preds'] = 0 | |
| p = len(df_test) | |
| model = ExponentialSmoothing(np.asarray(df_train['Page.Loads']),seasonal_periods=365, seasonal='add', trend='add') | |
| model_fit = model.fit() | |
| preds = model_fit.forecast(p) | |
| df_es['preds'] = preds |
Vishnunkumar
/ bubble_chart_ict.html
Created
November 28, 2020 11:24
plotly_bubble_chart_ict values of premier league players
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| <html> | |
| <head><meta charset="utf-8" /></head> | |
| <body> | |
| <div> <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script> | |
| <script type="text/javascript">/** | |
| * plotly.js v1.56.0 | |
| * Copyright 2012-2020, Plotly, Inc. | |
| * All rights reserved. | |
| * Licensed under the MIT license | |
| */ |
Vishnunkumar
/ transformers.py
Created
January 2, 2021 14:51
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| from simpletransformers.classification import ClassificationModel, ClassificationArgs | |
| model_args = ClassificationArgs() | |
| model_args.num_train_epochs = 4 | |
| model_args.reprocess_input_data = True | |
| model_args.save_best_model = True | |
| model_args.save_optimizer_and_scheduler = False | |
| model_args.overwrite_output_dir = True | |
| model_args.manual_seed = 4 | |
| model_args.use_multiprocessing = True |
Vishnunkumar
/ twitter_api.py
Last active
January 2, 2021 15:19
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| def create_twitter_url(handle, max_results): | |
| mrf = "max_results={}".format(max_results) | |
| q = "query=from:{}".format(handle) | |
| url = "https://api.twitter.com/2/tweets/search/recent?{}&{}".format( | |
| mrf, q | |
| ) | |
| return url | |
| def process_yaml(): |
Vishnunkumar
/ get_KG.py
Created
February 19, 2021 13:46
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| def get_KG(doc): | |
| text = [] | |
| for tok in doc: | |
| if tok.tag_ in ["NN","NNP","NNPS","NNS"]: | |
| text.append(tok.text) | |
| if tok.tag_ in ["VB","VBD","VBG","VBN","VBP","VBZ"]: | |
| text.append('<' + tok.text + '<') | |
| text = ('-').join(text) | |
| text_list = text.split('<') |
Vishnunkumar
/ query_kg.py
Created
February 19, 2021 14:29
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| def query(news_df, conf): | |
| try: | |
| conf = conf*(100) | |
| question = input() | |
| question_kg = get_KG(nlp(question)) | |
| query_param = [i for i,j in enumerate(question_kg) if j != ''] | |
| columns = ["subject", "links", "object"] | |
| col_dict = {} |
Vishnunkumar
/ sample.json
Last active
February 19, 2021 15:06
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| {"object": "patrolling island lack boats", | |
| "original-source": "Mumbai cops stop patrolling island due to lack of boats", | |
| "score": "100.0"} |
Vishnunkumar
/ graph.py
Created
February 19, 2021 15:25
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| kg_df = news_df.copy() | |
| G=nx.from_pandas_edgelist(kg_df[kg_df['links']=="declared"], "subject", "object", | |
| edge_attr=True, create_using=nx.MultiDiGraph()) | |
| plt.figure(figsize=(7, 7)) | |
| pos = nx.spring_layout(G, k = 0.5) | |
| nx.draw(G, with_labels=True, node_color='skyblue', node_size=700, edge_cmap=plt.cm.Blues, pos = pos) | |
| plt.show() | |
| plt.savefig('links.jpg') |
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