From e9843574754accf0b3b12ee45badda9a150a4a3f Mon Sep 17 00:00:00 2001 From: smit2k14 Date: Sun, 8 Mar 2020 22:57:51 +0530 Subject: [PATCH 1/6] Added a basic prediction model --- prediction/BitcoinPrices.csv | 1844 ++++++++++++++++++++++++++++++++++ prediction/main.py | 15 + prediction/model.py | 39 + prediction/utils.py | 28 + 4 files changed, 1926 insertions(+) create mode 100644 prediction/BitcoinPrices.csv create mode 100644 prediction/main.py create mode 100644 prediction/model.py create mode 100644 prediction/utils.py diff --git a/prediction/BitcoinPrices.csv b/prediction/BitcoinPrices.csv new file mode 100644 index 0000000..a51ae6e --- /dev/null +++ b/prediction/BitcoinPrices.csv @@ -0,0 +1,1844 @@ +Date,Symbol,Open,High,Low,Close,Volume BTC,Volume USD +2020-03-08,BTCUSD,8919,8919,8740.2,8755,1715.26,15087906.63 +2020-03-07,BTCUSD,9180.6,9241.5,8867.4,8919,4488.29,40545731.4 +2020-03-06,BTCUSD,9093.6,9199.7,9011.3,9180.6,3132.13,28588025.58 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+2015-03-06,BTCUSD,278.51,279.75,268.8,273.89,40490.63,11083898.02 +2015-03-05,BTCUSD,273.74,284.99,262,278.51,67235.69,18281541.95 +2015-03-04,BTCUSD,283.2,285.99,267,273.74,71328.67,19833718.19 +2015-03-03,BTCUSD,277.3,294,267.04,283.2,75940.04,21157890.72 +2015-03-02,BTCUSD,262.47,276,259.6,275.99,35351.14,9448438.14 +2015-03-01,BTCUSD,255.7,265,245.65,261.15,24361.06,6156716.71 +2015-02-28,BTCUSD,255.05,257.33,251.25,255.77,10695.34,2721373.29 +2015-02-27,BTCUSD,237,262.8,236.64,256.4,69269.58,17536633.13 +2015-02-26,BTCUSD,238,238.34,233.62,237,18691.43,4418655.89 +2015-02-25,BTCUSD,239.68,240.83,235.53,238,13574.5,3230232.16 +2015-02-24,BTCUSD,239.73,240.99,236.7,239.68,16784.31,4013461.44 +2015-02-23,BTCUSD,236.59,241,232.61,239.73,23893.09,5666261.84 +2015-02-22,BTCUSD,245.1,247.78,232.1,236.59,33394.5,7971863.56 +2015-02-21,BTCUSD,245.53,247.73,243.28,245.1,16514.04,4052466.26 +2015-02-20,BTCUSD,241.9,248.98,238.95,245.53,33337.97,8136342.67 +2015-02-19,BTCUSD,234.8,243.42,234.62,241.9,22792.55,5446200.04 +2015-02-18,BTCUSD,242.87,244.99,231.01,234.8,38588.38,9164141.83 +2015-02-17,BTCUSD,236.78,246.28,231.5,242.87,42669.08,10228035.45 +2015-02-16,BTCUSD,234.14,243.65,228.62,236.78,45750.85,10798751.12 +2015-02-15,BTCUSD,257,266.84,228.2,234.14,71361.28,17384420.7 +2015-02-14,BTCUSD,236.13,261.3,235.51,258.12,42984.94,10739014.48 +2015-02-13,BTCUSD,222.39,241.76,221.46,236,29079.11,6806972.89 +2015-02-12,BTCUSD,219.19,223.2,217.87,222.6,11190.65,2468883.37 +2015-02-11,BTCUSD,220.96,224.4,218.1,219.19,19289.28,4268986.79 +2015-02-10,BTCUSD,220.61,223.88,214,220.96,29268.95,6402350.57 +2015-02-09,BTCUSD,224.22,225,215.4,220.61,29625.03,6493501.42 diff --git a/prediction/main.py b/prediction/main.py new file mode 100644 index 0000000..0a556f9 --- /dev/null +++ b/prediction/main.py @@ -0,0 +1,15 @@ +from utils import * +from model import * + +if __name__ == '__main__': + bitcoin_prices = pd.read_csv('BitcoinPrices.csv') + mean_prices = rolling_mean(bitcoin_prices['Close'].values[::-1]) + mean_prices = mean_prices - np.mean(mean_prices) + mean_prices = mean_prices/np.std(mean_prices) + train_btc_price, test_btc_price = get_training_matrix(mean_prices) + model = BTCPrice(10, 5, 1) + loss = nn.MSELoss() + optimizer = optim.Adam(model.parameters()) + train(model = model, train_data = train_btc_price, test_data = test_btc_price, loss = loss, optimizer = optimizer) + torch.save(model, 'model.pt') + diff --git a/prediction/model.py b/prediction/model.py new file mode 100644 index 0000000..a6dcd86 --- /dev/null +++ b/prediction/model.py @@ -0,0 +1,39 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import torch +import torch.nn as nn +from torch.autograd import Variable +import torch.optim as optim + + +class BTCPrice(nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super(BTCPrice, self).__init__() + self.input_size = input_size + self.hidden_size = hidden_size + self.output_size = output_size + self.lstm = nn.LSTMCell(self.input_size, self.hidden_size) + self.tanh = nn.Tanh() + self.linear = nn.Linear(self.hidden_size, self.output_size) + + def forward(self, input): + out, hidden = self.lstm(input) + out = self.linear(out) + return out + + +def train(model, train_data, test_data, loss, optimizer, batch_size = 32, n_epochs = 1000): + for epoch in range(n_epochs): + t_loss = 0 + for i in range(0, len(train_data), batch_size): + optimizer.zero_grad() + train_X = Variable(torch.from_numpy(train_data[i:min(i+batch_size, len(train_data))]).float()) + train_Y = Variable(torch.from_numpy(test_data[i:min(i+batch_size, len(test_data))]).float()) + out_X = model.forward(train_X) + mse_loss = loss(out_X, train_Y) + mse_loss.backward() + optimizer.step() + t_loss+=mse_loss + print("Epoch {} | Loss {}".format(epoch+1, t_loss)) + diff --git a/prediction/utils.py b/prediction/utils.py new file mode 100644 index 0000000..9dd966e --- /dev/null +++ b/prediction/utils.py @@ -0,0 +1,28 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt + +def rolling_mean(input, last_items=5): + output = [] + for i in range(len(input)): + sum = 0 + for j in range(max(0, i-last_items), i+1): + sum+=input[j] + sum/=min(i+1, last_items) + output.append(sum) + return np.array(output) + +def get_training_matrix(input, last_items = 10): + output = [] + output1 = [] + for i in range(len(input)): + temp_output = [] + for j in range(max(0, i-last_items), i): + temp_output.append(input[j]) + if i-last_items < 0: + for j in range(last_items-i): + temp_output.append(input[i]) + output.append(temp_output) + output1.append(input[i]) + return np.array(output), np.array(output1) + From 0784f31ce0e7f12216c76b9435e62bc41894e891 Mon Sep 17 00:00:00 2001 From: smit2k14 Date: Wed, 11 Mar 2020 02:27:34 +0530 Subject: [PATCH 2/6] Added sentiment analysis model. --- .gitignore | 2 ++ sentiment/main.py | 84 ++++++++++++++++++++++++++++++++++++++++++++++ sentiment/model.py | 46 +++++++++++++++++++++++++ sentiment/utils.py | 51 ++++++++++++++++++++++++++++ 4 files changed, 183 insertions(+) create mode 100644 sentiment/main.py create mode 100644 sentiment/model.py create mode 100644 sentiment/utils.py diff --git a/.gitignore b/.gitignore index 00cbbdf..5381f0f 100644 --- a/.gitignore +++ b/.gitignore @@ -57,3 +57,5 @@ typings/ # dotenv environment variables file .env +__pycache__ +*.csv \ No newline at end of file diff --git a/sentiment/main.py b/sentiment/main.py new file mode 100644 index 0000000..85b5896 --- /dev/null +++ b/sentiment/main.py @@ -0,0 +1,84 @@ +from utils import * +from model import * +from sklearn.model_selection import train_test_split +import pandas as pd +import time + +if __name__ == '__main__': + TwComments = pd.read_csv('tweetsbitcoin.csv',delimiter=",", index_col=None) + TwComments = TwComments.dropna() + TwComments=TwComments.drop_duplicates() + TwComments=TwComments.sort_values(['date','time'], ascending=[True,True]) + TwComments["DateTime"]=pd.to_datetime(TwComments['date'] + ' ' + TwComments['time']) + TwComments["hour"] = TwComments.DateTime.dt.hour + TwComments["day"] = TwComments.DateTime.dt.weekday_name + TwComments["DateTime"] = TwComments.DateTime.values.astype(np.int64) // 10 ** 9 + TwComments["TimeDiff"]= TwComments["DateTime"] - (TwComments["DateTime"] % 3600) #when time diff is 2.00pm it will categorize comments between 2.00 - 2.59pm + TwComments = TwComments[TwComments.TimeDiff > 0] + + startTime = int(round(time.time()*60)) + tcomm=pd.DataFrame() + grouped_terms = TwComments.groupby(["TimeDiff"]) + + #This is done to combine the tweets in that hour into a list + i = 0 + tweets = [] + for name, group in grouped_terms: + t = [] + for row, data in group.iterrows(): + t.append(data['tweet']) + tweets.append(''.join(t)) + + #Formatting price into Y_train + Price = pd.read_csv ('binanceprices.csv',delimiter="\t", index_col=None) + Price["Date"] = pd.to_datetime(Price['ClTime'], unit = 'ms') + Price = Price.sort_values(['OpTime'], ascending=True) + high_prices = Price.loc[:,'High'].values + low_prices = Price.loc[:,'Low'].values + Price['Mid_Price'] = (high_prices+low_prices)/2.0 + + Price['Log_Ret'] = np.log(Price.Mid_Price) - np.log(Price.Mid_Price.shift(1)) + Price['Price Diff']= (Price.Mid_Price) - (Price.Mid_Price.shift(1)) + Price=Price.dropna() + + Price.loc[Price['Price Diff'] < 0, 'Price Diff'] = 0 + Price.loc[Price['Price Diff'] > 0, 'Price Diff'] = 1 + + Price1 = Price[['OpTime','Log_Ret','Price Diff']] + Price1['Log_Ret']=np.square(Price1[['Log_Ret']]) + Price1['OpTime']= Price1['OpTime'].floordiv(1000) + + ProcComm = pd.read_csv('OldTwDocTMatrix1.3.csv',delimiter=",") + date_time = ProcComm['Date'] + df = pd.DataFrame({'OpTime':date_time,'tweets':tweets}) + df = Price1.merge(df, on='OpTime') + df = df[['tweets', 'Price Diff']] + + df['tweets'] = df['tweets'].apply(lambda x: preprocess(x)) + df['tweets'] = df['tweets'].apply(lambda x: tokenizer(x)) + + tweets_1 = df['tweets'] + final_tweets = [] + for t in tweets_1: + t.insert(0, '') + t.append('') + final_tweets.append(t) + + w2i = {} + word_indices = [] + count = 0 + for t in final_tweets: + ind, w2i, count = word2index(t, w2i, count) + word_indices.append(ind) + + padded_word_indices = pad_sequences(word_indices) + X_train, X_test, y_train, y_test = train_test_split(padded_word_indices, df['Price Diff'].values, test_size=0.1, shuffle=True) + X_train = Variable(torch.from_numpy(X_train).long()) + y_train = Variable(torch.from_numpy(y_train).float()) + + model = SimpleClassifier(100, 32) + print(model.parameters()) + mseLoss = nn.MSELoss() + optimizer = optim.Adam(model.parameters()) + model = train(model, X_train, y_train, mseLoss, optimizer, batch_size = 16, n_epochs = 1) + diff --git a/sentiment/model.py b/sentiment/model.py new file mode 100644 index 0000000..e038820 --- /dev/null +++ b/sentiment/model.py @@ -0,0 +1,46 @@ +import numpy as np +import torch +import torch.optim as optim +import torch.nn as nn +from torch.autograd import Variable + + +class SimpleClassifier(nn.Module): + def __init__(self, input_size, hidden_size): + super(SimpleClassifier, self).__init__() + self.hidden_size = hidden_size + self.embedding = nn.Embedding(300000, hidden_size) + self.gru = nn.GRU(input_size = hidden_size, hidden_size = hidden_size) + self.linear = nn.Linear(hidden_size, 1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU() + + def forward(self, input, hidden): + embedded = self.embedding(input) + output = embedded + output, hidden = self.gru(output, hidden) + output = self.relu(output) + output = self.linear(output) + output = self.sigmoid(output) + return output, hidden + + def initHidden(self): + return torch.zeros(1, 100, self.hidden_size) + +def train(model, X_train, y_train, loss, optimizer, n_epochs = 10, batch_size = 32): + hidden = model.initHidden() + for epoch in range(n_epochs): + t_loss = 0 + for i in range(0, len(X_train), batch_size): + mse_loss = 0 + train_data = X_train[i:min(i+batch_size, len(X_train))] + train_labels = y_train[i:min(i+batch_size, len(y_train))] + pred, hidden = model.forward(train_data, hidden) + hidden = hidden.detach() + mse_loss += loss(pred, train_labels) + optimizer.zero_grad() + mse_loss.backward() + optimizer.step() + t_loss += mse_loss + print("Epoch {} | Loss {}".format(epoch+1, t_loss)) + return model \ No newline at end of file diff --git a/sentiment/utils.py b/sentiment/utils.py new file mode 100644 index 0000000..acc2ab7 --- /dev/null +++ b/sentiment/utils.py @@ -0,0 +1,51 @@ +import nltk +import numpy as np +import html,re +from nltk.stem.wordnet import WordNetLemmatizer + + +def preprocess(tweets): + t = html.unescape(tweets) + z = lambda x: re.compile('\#').sub('', re.compile('RT @').sub('@', x).strip()) + t = z(t) + tweet = ' '.join(re.sub("(@[_A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)"," ",t).split()) + return tweet + +def tokenizer(tweets): + tweet = " ".join(re.split("[^a-zA-Z.,!?]*", tweets.lower())).strip() + lemmatizer = WordNetLemmatizer() + tweets = [lemmatizer.lemmatize(tweet.lower()) for tweet in tweets.split()] + return tweets + +def basic_tokenizer(tweet): + #Same as tokenize but without the stemmer + tweet = " ".join(re.split("[^a-zA-Z.,!?]*", tweet.lower())).strip() + return tweet.split() + +def word2index(sent, word_dict = {}, count = 1): + index = [] + if len(word_dict)==0: + word_dict[''] = 1 + word_dict[''] = 2 + count = 3 + + for word in sent: + if word in word_dict: + index.append(word_dict[word]) + else: + word_dict[word] = count + index.append(count) + count+=1 + return index, word_dict, count + +def pad_sequences(seq, max_length = 100): + out_seq = [] + for s in seq: + if len(s)>=max_length: + out_seq.append(s[:max_length]) + else: + s_len = len(s) + for j in range(max_length-s_len): + s.append(0) + out_seq.append(s) + return np.array(out_seq) \ No newline at end of file From d5f2202c96bb07ab93fc936bb7c8501b95f21691 Mon Sep 17 00:00:00 2001 From: smit2k14 Date: Thu, 12 Mar 2020 17:27:13 +0530 Subject: [PATCH 3/6] Added cuda support --- sentiment/main.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/sentiment/main.py b/sentiment/main.py index 85b5896..f2a9e94 100644 --- a/sentiment/main.py +++ b/sentiment/main.py @@ -3,8 +3,12 @@ from sklearn.model_selection import train_test_split import pandas as pd import time +import torch if __name__ == '__main__': + + device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + TwComments = pd.read_csv('tweetsbitcoin.csv',delimiter=",", index_col=None) TwComments = TwComments.dropna() TwComments=TwComments.drop_duplicates() @@ -73,10 +77,10 @@ padded_word_indices = pad_sequences(word_indices) X_train, X_test, y_train, y_test = train_test_split(padded_word_indices, df['Price Diff'].values, test_size=0.1, shuffle=True) - X_train = Variable(torch.from_numpy(X_train).long()) - y_train = Variable(torch.from_numpy(y_train).float()) + X_train = Variable(torch.from_numpy(X_train).long().device()) + y_train = Variable(torch.from_numpy(y_train).float().device()) - model = SimpleClassifier(100, 32) + model = SimpleClassifier(100, 32).device() print(model.parameters()) mseLoss = nn.MSELoss() optimizer = optim.Adam(model.parameters()) From d31cad8e019f0ae4d7f77770468d13168abca62c Mon Sep 17 00:00:00 2001 From: smit2k14 Date: Mon, 6 Apr 2020 14:50:35 +0530 Subject: [PATCH 4/6] Created API template for prediction (not final) --- .gitignore | 4 ++ api/app.py | 28 +++++++++++- sentiment/crypto_history.py | 83 ++++++++++++++++++++++++++++++++++++ sentiment/install_locally.py | 25 +++++++++++ sentiment/main.py | 22 +++++----- sentiment/model.py | 4 +- 6 files changed, 152 insertions(+), 14 deletions(-) create mode 100644 sentiment/crypto_history.py create mode 100644 sentiment/install_locally.py diff --git a/.gitignore b/.gitignore index b33dd25..e9196d6 100644 --- a/.gitignore +++ b/.gitignore @@ -60,6 +60,7 @@ typings/ # Model Training __pycache__ *.csv +*.pkl # python venv @@ -72,3 +73,6 @@ venv # AWS .aws + +#ML +*.pt \ No newline at end of file diff --git a/api/app.py b/api/app.py index 95d487f..5c06d41 100644 --- a/api/app.py +++ b/api/app.py @@ -8,7 +8,9 @@ import boto3 import time import hashlib - +import torch +import numpy as np +from torch import Variable # import ssl # ssl._create_default_https_context = ssl._create_unverified_context @@ -861,5 +863,29 @@ def sendTempData(): } return jsonify(data) + +# ML - API + +#Prediction for the next k-datapoints +@app.route('/ml/prediction/') +@cross_origin() +def prediction(k_data): + #k_data (json) is the data of the previous k data-points of the given currenct + #k is determined by the number of previous data used for training (Currently k = 5) + m = int(k_data['next']) + k_data = np.array(k_data['data']) + #Model directory here + model = torch.load('../prediction/model.pt') + output = [] + with torch.no_grad(): + for i in range(m): + data = Variable(torch.from_numpy(k_data)) + out = model.forward(data)[0].cpu().float().numpy() + k_data.append(out) + output.append(out) + k_data = k_data[1:-1] + return jsonify(output) + + if __name__ == '__main__': app.run() \ No newline at end of file diff --git a/sentiment/crypto_history.py b/sentiment/crypto_history.py new file mode 100644 index 0000000..a389d74 --- /dev/null +++ b/sentiment/crypto_history.py @@ -0,0 +1,83 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +"""Script to gather historical cryptocurrency data from coinmarketcap.com (cmc) """ + +import json +import requests +from bs4 import BeautifulSoup +import csv +import sys +from time import sleep + + +def CoinNames(): + """Gets ID's of all coins on cmc""" + + names = [] + response = requests.get("https://api.coinmarketcap.com/v1/ticker/?limit=0") + respJSON = json.loads(response.text) + for i in respJSON: + names.append(i['id']) + return names + +def gather(startdate, enddate, names): + historicaldata = [] + counter = 1 + + if len(names) == 0: + names = CoinNames() + + for coin in names: + sleep(10) + r = requests.get("https://coinmarketcap.com/currencies/{0}/historical-data/?start={1}&end={2}".format(coin, startdate, enddate)) + data = r.text + soup = BeautifulSoup(data, "html.parser") + table = soup.find('table', attrs={ "class" : "table"}) + + #Add table header to list + if len(historicaldata) == 0: + headers = [header.text for header in table.find_all('th')] + headers.insert(0, "Coin") + + for row in table.find_all('tr'): + currentrow = [val.text for val in row.find_all('td')] + if(len(currentrow) != 0): + currentrow.insert(0, coin) + historicaldata.append(currentrow) + + print("Coin Counter -> " + str(counter), end='\r') + counter += 1 + return headers, historicaldata + +def _gather(startdate, enddate): + """ Scrape data off cmc""" + + if(len(sys.argv) == 3): + names = CoinNames() + else: + names = [sys.argv[3]] + + headers, historicaldata = gather(startdate, enddate, names) + + Save(headers, historicaldata) + +def Save(headers, rows): + + if(len(sys.argv) == 3): + FILE_NAME = "HistoricalCoinData.csv" + else: + FILE_NAME = sys.argv[3] + ".csv" + + with open(FILE_NAME, 'w') as f: + writer = csv.writer(f) + writer.writerow(headers) + writer.writerows(row for row in rows if row) + print("Finished!") + +if __name__ == "__main__": + + startdate = sys.argv[1] + enddate = sys.argv[2] + + _gather(startdate, enddate) + diff --git a/sentiment/install_locally.py b/sentiment/install_locally.py new file mode 100644 index 0000000..b297d25 --- /dev/null +++ b/sentiment/install_locally.py @@ -0,0 +1,25 @@ +from __future__ import print_function +import os, site + +#package = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'packages') +package = os.path.dirname(os.path.abspath(__file__)) +print(package) + +pathspec = r""" +# Generated by Crypto-History's installer (install_locally.py) +# In the lines below, list the paths where Python should look for +# supplied modules, one directory per line. +# +# If a directory does not exist when Python is started, it will be ignored. +%s +""" % package + +print("Adding path:", package) + +usp = site.getusersitepackages() +if not os.path.exists(usp): + os.makedirs(usp) +uspfile = os.path.join(usp, 'crypto-history.pth') +open(uspfile, 'w').write(pathspec) +print('Wrote to ' + uspfile) +print("Crypto-history package installed successfully!") diff --git a/sentiment/main.py b/sentiment/main.py index c0efc8f..006b688 100644 --- a/sentiment/main.py +++ b/sentiment/main.py @@ -3,17 +3,12 @@ from sklearn.model_selection import train_test_split import pandas as pd import time -<<<<<<< HEAD import torch if __name__ == '__main__': - device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") - -======= - -if __name__ == '__main__': ->>>>>>> 40cde8f9b7a7dae1cb782c3cd2937f7f0dd5af88 + #device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") + ''' TwComments = pd.read_csv('tweetsbitcoin.csv',delimiter=",", index_col=None) TwComments = TwComments.dropna() TwComments=TwComments.drop_duplicates() @@ -53,7 +48,7 @@ Price.loc[Price['Price Diff'] < 0, 'Price Diff'] = 0 Price.loc[Price['Price Diff'] > 0, 'Price Diff'] = 1 - Price1 = Price[['OpTime','Log_Ret','Price Diff']] + Price1 = Price[['OpTime','Log_Ret','Price ']] Price1['Log_Ret']=np.square(Price1[['Log_Ret']]) Price1['OpTime']= Price1['OpTime'].floordiv(1000) @@ -67,6 +62,8 @@ df['tweets'] = df['tweets'].apply(lambda x: tokenizer(x)) tweets_1 = df['tweets'] + print(tweets_1) + exit() final_tweets = [] for t in tweets_1: t.insert(0, '') @@ -84,10 +81,13 @@ X_train, X_test, y_train, y_test = train_test_split(padded_word_indices, df['Price Diff'].values, test_size=0.1, shuffle=True) X_train = Variable(torch.from_numpy(X_train).long().device()) y_train = Variable(torch.from_numpy(y_train).float().device()) + ''' - model = SimpleClassifier(100, 32).device() - print(model.parameters()) + model = SimpleClassifier1(100, 32) + #model.save('sentiment.pt') + #print(model.parameters()) mseLoss = nn.MSELoss() optimizer = optim.Adam(model.parameters()) - model = train(model, X_train, y_train, mseLoss, optimizer, batch_size = 16, n_epochs = 1) + torch.save(model, 'sentiment.pt') + #model = train(model, X_train, y_train, mseLoss, optimizer, batch_size = 16, n_epochs = 1) diff --git a/sentiment/model.py b/sentiment/model.py index e038820..5ac3492 100644 --- a/sentiment/model.py +++ b/sentiment/model.py @@ -5,9 +5,9 @@ from torch.autograd import Variable -class SimpleClassifier(nn.Module): +class SimpleClassifier1(nn.Module): def __init__(self, input_size, hidden_size): - super(SimpleClassifier, self).__init__() + super(SimpleClassifier1, self).__init__() self.hidden_size = hidden_size self.embedding = nn.Embedding(300000, hidden_size) self.gru = nn.GRU(input_size = hidden_size, hidden_size = hidden_size) From 109353adbc5cbb341247fc9ea3462ddb2a540779 Mon Sep 17 00:00:00 2001 From: smit2k14 Date: Mon, 6 Apr 2020 17:06:54 +0530 Subject: [PATCH 5/6] changed requirements.txt for flask --- api/requirements.txt | 2 ++ 1 file changed, 2 insertions(+) diff --git a/api/requirements.txt b/api/requirements.txt index 6d68e5a..50c2a90 100644 --- a/api/requirements.txt +++ b/api/requirements.txt @@ -22,6 +22,7 @@ Jinja2==2.11.1 jmespath==0.9.5 MarkupSafe==1.1.1 more-itertools==5.0.0 +numpy==1.17.2 packaging==20.3 pathlib2==2.3.5 pluggy==0.13.1 @@ -36,6 +37,7 @@ s3transfer==0.3.3 scandir==1.10.0 six==1.14.0 soupsieve==1.9.5 +torch==1.2.0 typing==3.7.4.1 urllib3==1.25.8 wcwidth==0.1.9 From 4f0a548e672b8d97d89700eb832cbcfac6a6037f Mon Sep 17 00:00:00 2001 From: smit2k14 Date: Tue, 7 Apr 2020 12:35:49 +0530 Subject: [PATCH 6/6] Changed requirements.txt for a suitable version of numpy --- api/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/api/requirements.txt b/api/requirements.txt index f6541cb..e4a3262 100644 --- a/api/requirements.txt +++ b/api/requirements.txt @@ -22,7 +22,7 @@ Jinja2==2.11.1 jmespath==0.9.5 MarkupSafe==1.1.1 more-itertools==5.0.0 -numpy==1.17.2 +numpy==1.16.6 packaging==20.3 pathlib2==2.3.5 pluggy==0.13.1