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korne 2022-06-15 00:07:45 +02:00
parent db6d196edb
commit f140a121a2
4 changed files with 3280 additions and 194 deletions
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124
dev-0/out.tsv
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181
run.ipynb
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@ -16,47 +16,25 @@
"from nltk import word_tokenize"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cbe60d7b-850e-4838-b4ce-672f13bf2bb2",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"id": "bf211ece-e27a-4119-a1b9-9a9a610cfb46",
"id": "1ec57d97-a852-490e-8da4-d1e4c9676cd6",
"metadata": {},
"outputs": [],
"source": [
"def predict_year(x, path_out, model):\n",
" results = model.predict(x)\n",
" with open(path_out, 'wt') as file:\n",
" for r in results:\n",
" file.write(str(r) + '\\n') "
"#def read_file(filename):\n",
"# result = []\n",
"# with open(filename, 'r', encoding=\"utf-8\") as file:\n",
"# for line in file:\n",
"# text = line.split(\"\\t\")[0].strip()\n",
"# result.append(text)\n",
"# return result"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1ec57d97-a852-490e-8da4-d1e4c9676cd6",
"metadata": {},
"outputs": [],
"source": [
"def read_file(filename):\n",
" result = []\n",
" with open(filename, 'r', encoding=\"utf-8\") as file:\n",
" for line in file:\n",
" text = line.split(\"\\t\")[0].strip()\n",
" result.append(text)\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "86fbfb79-76e7-49f5-b722-2827f93cb03f",
"metadata": {},
"outputs": [
@ -163,7 +141,7 @@
"[200000 rows x 2 columns]"
]
},
"execution_count": 4,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@ -176,7 +154,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 4,
"id": "8960c975-f756-4e36-a1ce-e9fd5fdf8fe3",
"metadata": {},
"outputs": [
@ -271,7 +249,7 @@
"[200000 rows x 1 columns]"
]
},
"execution_count": 5,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@ -285,7 +263,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 5,
"id": "6b27e6ce-e9fd-41a1-aacf-53a5fde0a7c1",
"metadata": {},
"outputs": [
@ -392,7 +370,7 @@
"[5272 rows x 2 columns]"
]
},
"execution_count": 6,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@ -405,7 +383,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 6,
"id": "99ae526d-9b7c-493f-be4f-f95b1c8f4b81",
"metadata": {},
"outputs": [
@ -512,7 +490,7 @@
"[5152 rows x 2 columns]"
]
},
"execution_count": 7,
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@ -525,7 +503,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 7,
"id": "dba17668-971f-47f8-99ce-fc840b5cb74a",
"metadata": {},
"outputs": [],
@ -546,7 +524,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 8,
"id": "1a275c1d-75bc-4290-9332-56396d16a0f2",
"metadata": {},
"outputs": [],
@ -558,15 +536,48 @@
"\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]"
"x_test = [word_tokenize(x) for x in x_test]\n",
"#x_test"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 9,
"id": "3125d2f2-0da9-45eb-acf1-90293c6d64a3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5152"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "031a3670-3be7-4146-97b4-0dacd4f9ae58",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"5152"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from gensim.test.utils import common_texts\n",
"from gensim.models import Word2Vec\n",
@ -574,24 +585,16 @@
"word2vec = gensim.downloader.load('word2vec-google-news-300')\n",
"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]\n",
"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]\n",
"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]"
"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]\n",
"len(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 15,
"id": "b7defd18-e281-4cf6-9941-cee560749677",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\korne\\AppData\\Local\\Temp\\ipykernel_22024\\3484013121.py:10: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\torch\\csrc\\utils\\tensor_new.cpp:210.)\n",
" X = torch.tensor(X)\n"
]
}
],
"outputs": [],
"source": [
"model = NeuralNetworkModel()\n",
"BATCH_SIZE = 5\n",
@ -614,10 +617,21 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 17,
"id": "92c69ddd-fe58-477f-b2c2-06324a983bcc",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"5152"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_dev = []\n",
"y_test = []\n",
@ -635,60 +649,51 @@
" 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 += prediction.tolist()"
" prediction = (outputs >= 0.5)\n",
" y_test += prediction.tolist()\n",
"len(y_test)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 13,
"id": "caff921c-d0ab-4fce-a17f-6610266b404d",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"2062"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_dev = np.asarray(y_dev, dtype=np.int32)\n",
"y_test = np.asarray(y_test, dtype=np.int32)"
"y_test = np.asarray(y_test, dtype=np.int32)\n",
"len(y_test)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"id": "73076eb2-810f-4f85-aa3f-05ee884c413b",
"metadata": {},
"outputs": [],
"source": [
"with open('./dev-0/out.tsv', 'wt') as file:\n",
" for r in y_dev:\n",
" file.write(str(r) + '\\n') "
"y_dev.tofile('./dev-0/out.tsv', sep='\\n')\n",
"y_test.tofile('./test-A/out.tsv', sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ddda251c-cafa-40f8-a020-48310a9f23b6",
"metadata": {},
"outputs": [],
"source": [
"with open('./test-A/out.tsv', 'wt') as file:\n",
" for r in y_test:\n",
" file.write(str(r) + '\\n') "
]
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"id": "5730562a-0200-4c8f-8f73-992fa2b36133",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook run.ipynb to script\n",
"[NbConvertApp] Writing 3816 bytes to run.py\n"
]
}
],
"outputs": [],
"source": [
"!jupyter nbconvert --to script run.ipynb"
]

79
run.py
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@ -13,43 +13,27 @@ import gensim.downloader
from nltk import word_tokenize
# In[ ]:
# In[2]:
def predict_year(x, path_out, model):
results = model.predict(x)
with open(path_out, 'wt') as file:
for r in results:
file.write(str(r) + '\n')
#def read_file(filename):
# result = []
# with open(filename, 'r', encoding="utf-8") as file:
# for line in file:
# text = line.split("\t")[0].strip()
# result.append(text)
# return result
# In[3]:
def read_file(filename):
result = []
with open(filename, 'r', encoding="utf-8") as file:
for line in file:
text = line.split("\t")[0].strip()
result.append(text)
return result
# In[4]:
x_train = pd.read_table('train/in.tsv', sep='\t', header=None, quoting=3)
x_train = x_train[0:200000]
x_train
# In[5]:
# In[4]:
with open('train/expected.tsv', 'r', encoding='utf8') as file:
@ -58,7 +42,7 @@ y_train = y_train[0:200000]
y_train
# In[6]:
# In[5]:
with open('dev-0/in.tsv', 'r', encoding='utf8') as file:
@ -66,7 +50,7 @@ with open('dev-0/in.tsv', 'r', encoding='utf8') as file:
x_dev
# In[7]:
# In[6]:
with open('test-A/in.tsv', 'r', encoding='utf8') as file:
@ -74,7 +58,7 @@ with open('test-A/in.tsv', 'r', encoding='utf8') as file:
x_test
# In[8]:
# In[7]:
class NeuralNetworkModel(torch.nn.Module):
@ -91,7 +75,7 @@ class NeuralNetworkModel(torch.nn.Module):
return x
# In[9]:
# In[8]:
x_train = x_train[0].str.lower()
@ -102,9 +86,16 @@ x_test = x_test[0].str.lower()
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_test
# In[11]:
# In[9]:
len(x_test)
# In[10]:
from gensim.test.utils import common_texts
@ -114,9 +105,10 @@ word2vec = gensim.downloader.load('word2vec-google-news-300')
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]
len(x_test)
# In[ ]:
# In[15]:
model = NeuralNetworkModel()
@ -138,7 +130,7 @@ for epoch in range(BATCH_SIZE):
optimizer.step()
# In[ ]:
# In[17]:
y_dev = []
@ -157,31 +149,24 @@ with torch.no_grad():
X = x_test[i:i + BATCH_SIZE]
X = torch.tensor(X)
outputs = model(X.float())
y = (outputs >= 0.5)
prediction = (outputs >= 0.5)
y_test += prediction.tolist()
len(y_test)
# In[ ]:
# In[13]:
y_dev = np.asarray(y_dev, dtype=np.int32)
y_test = np.asarray(y_test, dtype=np.int32)
len(y_test)
# In[ ]:
with open('./dev-0/out.tsv', 'wt') as file:
for r in y_dev:
file.write(str(r) + '\n')
# In[ ]:
with open('./test-A/out.tsv', 'wt') as file:
for r in y_test:
file.write(str(r) + '\n')
y_dev.tofile('./dev-0/out.tsv', sep='\n')
y_test.tofile('./test-A/out.tsv', sep='\n')
# In[ ]:
@ -189,3 +174,9 @@ with open('./test-A/out.tsv', 'wt') as file:
get_ipython().system('jupyter nbconvert --to script run.ipynb')
# In[ ]:

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