s460939/paranormal-or-skeptic-ISI-public
1
0
Fork 0
forked from kubapok/paranormal-or-skeptic-ISI-public
Code Issues Pull Requests Projects Releases Wiki Activity

Compare commits

base: s460939:master
Branches Tags
s460939:master
kubapok:master
..
compare: kubapok:master
Branches Tags
kubapok:master
s460939:master

No commits in common. "master" and "master" have entirely different histories.
master ... master

10 changed files with 0 additions and 311135 deletions
Show Stats Expand all files Collapse all files

139
Naiwny_bayes.ipynb
View File

@ -1,139 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"import sklearn\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"import numpy as np\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.preprocessing import LabelEncoder "
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"def getInput(path):\n",
" with open(path,encoding='utf-8') as f:\n",
" return f.readlines()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/c/Users/mkoci/Desktop/naiwny_bayes\n"
]
}
],
"source": [
"!pwd"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"train_in=getInput('./train/in.tsv')\n",
"train_expected=getInput('./train/expected.tsv')\n",
"test_in=getInput('./test-A/in.tsv')\n",
"dev_in=getInput('./dev-0/in.tsv')\n",
"dev_expected=getInput('./dev-0/expected.tsv')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())\n",
"encTransform = LabelEncoder().fit_transform(train_expected)\n",
"model = pipeline.fit(train_in, encTransform)\n",
"dev_predicted = model.predict(dev_in)\n",
"test_predicted = model.predict(test_in)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"with open('./dev-0/out.tsv', \"w\") as result:\n",
" for out in dev_predicted:\n",
" result.write(str(out) + '\\n')\n",
"with open('./test-A/out.tsv', \"w\") as result:\n",
" for out in test_predicted:\n",
" result.write(str(out) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook Naiwny_bayes.ipynb to script\n",
"[NbConvertApp] Writing 1337 bytes to Naiwny_bayes.py\n"
]
}
],
"source": [
"!jupyter nbconvert --to script Naiwny_bayes.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

76
Naiwny_bayes.py
View File

@ -1,76 +0,0 @@
#!/usr/bin/env python
# coding: utf-8
# In[46]:
import sklearn
from sklearn.pipeline import make_pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from sklearn.naive_bayes import MultinomialNB
from sklearn.preprocessing import LabelEncoder
# In[47]:
def getInput(path):
with open(path,encoding='utf-8') as f:
return f.readlines()
# In[48]:
get_ipython().system('pwd')
# In[49]:
train_in=getInput('./train/in.tsv')
train_expected=getInput('./train/expected.tsv')
test_in=getInput('./test-A/in.tsv')
dev_in=getInput('./dev-0/in.tsv')
dev_expected=getInput('./dev-0/expected.tsv')
# In[50]:
pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
encTransform = LabelEncoder().fit_transform(train_expected)
model = pipeline.fit(train_in, encTransform)
dev_predicted = model.predict(dev_in)
test_predicted = model.predict(test_in)
# In[ ]:
# In[54]:
with open('./dev-0/out.tsv', "w") as result:
for out in dev_predicted:
result.write(str(out) + '\n')
with open('./test-A/out.tsv', "w") as result:
for out in test_predicted:
result.write(str(out) + '\n')
# In[55]:
get_ipython().system('jupyter nbconvert --to script Naiwny_bayes.ipynb')
# In[ ]:

155
Untitled.ipynb
View File

@ -1,155 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def getInput(path):\n",
" with open(path,encoding='utf-8') as f:\n",
" return f.readlines()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import gensim.downloader as gensim\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"from nltk.tokenize import word_tokenize"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"word2vec = gensim.load('word2vec-google-news-300')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# train_in=getInput('./train/in.tsv')\n",
"# train_expected=getInput('./train/expected.tsv')\n",
"# test_in=getInput('./test-A/in.tsv')\n",
"# dev_in=getInput('./dev-0/in.tsv')\n",
"# dev_expected=getInput('./dev-0/expected.tsv')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"class NeuralNetworkModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super(NeuralNetworkModel, self).__init__()\n",
" self.l01 = torch.nn.Linear(300, 300)\n",
" self.l02 = torch.nn.Linear(300, 1)\n",
"\n",
" def forward(self, x):\n",
" x = self.l01(x)\n",
" x = torch.relu(x)\n",
" x = self.l02(x)\n",
" x = torch.sigmoid(x)\n",
" return x\n",
"\n",
"def d2v(doc):\n",
" return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)\n",
"x_train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\\t', header=None, error_bad_lines=False, quoting=3)\n",
"x_train = x_train[0].str.lower()\n",
"x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n",
"x_dev = x_dev[0].str.lower()\n",
"x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\\t', header=None, quoting=3)\n",
"x_test = x_test[0].str.lower()\n",
"y_train = pd.read_table('train/expected.tsv', sep='\\t', header=None, quoting=3)\n",
"y_train = y_train[0]\n",
"x_train = [word_tokenize(x) for x in x_train]\n",
"x_dev = [word_tokenize(x) for x in x_dev]\n",
"x_test = [word_tokenize(x) for x in x_test]\n",
"x_train = [d2v(doc) for doc in x_train]\n",
"x_dev = [d2v(doc) for doc in x_dev]\n",
"x_test = [d2v(doc) for doc in x_test]\n",
"model = NeuralNetworkModel()\n",
"BATCH_SIZE = 10\n",
"criterion = torch.nn.BCELoss()\n",
"optimizer = torch.optim.Adam(model.parameters())\n",
"for epoch in range(BATCH_SIZE):\n",
" model.train()\n",
" for i in range(0, y_train.shape[0], BATCH_SIZE):\n",
" X = x_train[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" y = y_train[i:i + BATCH_SIZE]\n",
" y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)\n",
" optimizer.zero_grad()\n",
" outputs = model(X.float())\n",
" loss = criterion(outputs, y)\n",
" loss.backward()\n",
" optimizer.step()\n",
"y_dev = []\n",
"y_test = []\n",
"model.eval()\n",
"with torch.no_grad():\n",
" for i in range(0, len(x_dev), BATCH_SIZE):\n",
" X = x_dev[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs > 0.5)\n",
" y_dev.extend(y)\n",
"\n",
" for i in range(0, len(x_test), BATCH_SIZE):\n",
" X = x_test[i:i + BATCH_SIZE]\n",
" X = torch.tensor(X)\n",
" outputs = model(X.float())\n",
" y = (outputs >= 0.5)\n",
" y_test.extend(y)\n",
"\n",
"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
"Y_dev = pd.DataFrame({'label': y_dev})\n",
"y_test = np.asarray(y_test, dtype=np.int32)\n",
"Y_test = pd.DataFrame({'label': y_test})\n",
"Y_dev.to_csv(r'dev-0/out.tsv', sep='\\t', index=False, header=False)\n",
"Y_test.to_csv(r'test-A/out.tsv', sep='\\t', index=False, header=False)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

116
Untitled.py
View File

@ -1,116 +0,0 @@
#!/usr/bin/env python
# coding: utf-8
# In[2]:
def getInput(path):
with open(path,encoding='utf-8') as f:
return f.readlines()
# In[6]:
import gensim.downloader as gensim
import numpy as np
import pandas as pd
import torch
from nltk.tokenize import word_tokenize
# In[8]:
word2vec = gensim.load('word2vec-google-news-300')
# In[5]:
# train_in=getInput('./train/in.tsv')
# train_expected=getInput('./train/expected.tsv')
# test_in=getInput('./test-A/in.tsv')
# dev_in=getInput('./dev-0/in.tsv')
# dev_expected=getInput('./dev-0/expected.tsv')
# In[14]:
class NeuralNetworkModel(torch.nn.Module):
def __init__(self):
super(NeuralNetworkModel, self).__init__()
self.l01 = torch.nn.Linear(300, 300)
self.l02 = torch.nn.Linear(300, 1)
def forward(self, x):
x = self.l01(x)
x = torch.relu(x)
x = self.l02(x)
x = torch.sigmoid(x)
return x
def d2v(doc):
return np.mean([word2vec[word] for word in doc if word in word2vec] or [np.zeros(300)], axis=0)
x_train = pd.read_table('train/in.tsv.xz', compression='xz', sep='\t', header=None, error_bad_lines=False, quoting=3)
x_train = x_train[0].str.lower()
x_dev = pd.read_table('dev-0/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
x_dev = x_dev[0].str.lower()
x_test = pd.read_table('test-A/in.tsv.xz', compression='xz', sep='\t', header=None, quoting=3)
x_test = x_test[0].str.lower()
y_train = pd.read_table('train/expected.tsv', sep='\t', header=None, quoting=3)
y_train = y_train[0]
x_train = [word_tokenize(x) for x in x_train]
x_dev = [word_tokenize(x) for x in x_dev]
x_test = [word_tokenize(x) for x in x_test]
x_train = [d2v(doc) for doc in x_train]
x_dev = [d2v(doc) for doc in x_dev]
x_test = [d2v(doc) for doc in x_test]
model = NeuralNetworkModel()
BATCH_SIZE = 10
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters())
for epoch in range(BATCH_SIZE):
model.train()
for i in range(0, y_train.shape[0], BATCH_SIZE):
X = x_train[i:i + BATCH_SIZE]
X = torch.tensor(X)
y = y_train[i:i + BATCH_SIZE]
y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
optimizer.zero_grad()
outputs = model(X.float())
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
y_dev = []
y_test = []
model.eval()
with torch.no_grad():
for i in range(0, len(x_dev), BATCH_SIZE):
X = x_dev[i:i + BATCH_SIZE]
X = torch.tensor(X)
outputs = model(X.float())
y = (outputs > 0.5)
y_dev.extend(y)
for i in range(0, len(x_test), BATCH_SIZE):
X = x_test[i:i + BATCH_SIZE]
X = torch.tensor(X)
outputs = model(X.float())
y = (outputs >= 0.5)
y_test.extend(y)
y_dev = np.asarray(y_dev, dtype=np.int32)
Y_dev = pd.DataFrame({'label': y_dev})
y_test = np.asarray(y_test, dtype=np.int32)
Y_test = pd.DataFrame({'label': y_test})
Y_dev.to_csv(r'dev-0/out.tsv', sep='\t', index=False, header=False)
Y_test.to_csv(r'test-A/out.tsv', sep='\t', index=False, header=False)
# In[ ]:

222
Untitled10-Copy1.py
View File

@ -1,222 +0,0 @@
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import csv
# In[2]:
get_ipython().system('pip install gensim')
# In[17]:
import nltk
nltk.download('punkt')
# In[9]:
get_ipython().system('pip install nltk')
# In[3]:
get_ipython().system('pip install torch')
# In[4]:
import gensim.downloader
import numpy as np
import pandas as pd
import torch
# In[5]:
import torch.nn as nn
from nltk import word_tokenize
# In[13]:
header_names = ["content", "id", "label"]
# In[23]:
class FF(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim):
super(FF, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim)
self.relu1 = nn.ReLU()
self.fc2 = nn.Linear(hidden_dim, hidden_dim)
self.relu2 = nn.ReLU()
self.fc3 = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
out = self.fc1(x)
out = self.relu1(out)
out = self.relu2(out)
out = self.fc3(out)
return torch.sigmoid(out)
train_set_labels = pd.read_table(
"train/expected.tsv",
error_bad_lines=False,
quoting=csv.QUOTE_NONE,
header=None,
names=header_names[2:],
)
train_set_features = pd.read_table(
"train/in.tsv.xz",
error_bad_lines=False,
quoting=csv.QUOTE_NONE,
header=None,
names=header_names[:2],
)
test_set = pd.read_table(
"test-A/in.tsv.xz",
error_bad_lines=False,
header=None,
quoting=csv.QUOTE_NONE,
names=header_names[:2],
)
dev_set = pd.read_table(
"dev-0/in.tsv.xz",
error_bad_lines=False,
header=None,
quoting=csv.QUOTE_NONE,
names=header_names[:2],
)
X_train = train_set_features["content"].str.lower()
y_train = train_set_labels["label"]
X_dev = dev_set["content"].str.lower()
X_test = test_set["content"].str.lower()
X_train = [word_tokenize(content) for content in X_train]
X_dev = [word_tokenize(content) for content in X_dev]
X_test = [word_tokenize(content) for content in X_test]
word2vec = gensim.downloader.load("word2vec-google-news-300")
# In[24]:
X_train = [
np.mean(
[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
axis=0,
)
for content in X_train
]
X_dev = [
np.mean(
[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
axis=0,
)
for content in X_dev
]
X_test = [
np.mean(
[word2vec[word] for word in content if word in word2vec] or [np.zeros(300)],
axis=0,
)
for content in X_test
]
hidden_layer = 650
epochs = 15
batch_size = 10
# In[27]:
output_dim = 1
input_dim =300
model = FF(input_dim, hidden_layer, output_dim)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = torch.nn.BCELoss()
# In[28]:
for epoch in range(epochs):
model.train()
for i in range(0, y_train.shape[0], batch_size):
X = X_train[i : i + batch_size]
X = torch.tensor(X)
y = y_train[i : i + batch_size]
y = torch.tensor(y.astype(np.float32).to_numpy()).reshape(-1, 1)
outputs = model(X.float())
loss = criterion(outputs, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
test_prediction = []
dev_prediction = []
model.eval()
with torch.no_grad():
for i in range(0, len(X_test), batch_size):
X = X_test[i : i + batch_size]
X = torch.tensor(X)
outputs = model(X.float())
prediction = outputs > 0.5
test_prediction += prediction.tolist()
for i in range(0, len(X_dev), batch_size):
X = X_dev[i : i + batch_size]
X = torch.tensor(X)
outputs = model(X.float())
prediction = outputs > 0.5
dev_prediction += prediction.tolist()
test_prediction = np.asarray(test_prediction, dtype=np.int32)
dev_prediction = np.asarray(dev_prediction, dtype=np.int32)
test_prediction.tofile("./test-A/out.tsv", sep="\n")
dev_prediction.tofile("./dev-0/out.tsv", sep="\n")
# In[ ]:
# In[ ]:
# In[ ]:

5272
dev-0/in.tsv
View File

File diff suppressed because one or more lines are too long

5272
dev-0/out.tsv
View File

File diff suppressed because it is too large Load Diff

5152
test-A/in.tsv
View File

File diff suppressed because one or more lines are too long

5152
test-A/out.tsv
View File

File diff suppressed because it is too large Load Diff

289579
train/in.tsv
View File

File diff suppressed because one or more lines are too long