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CONDA small refactoring

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This commit is contained in:
Mikołaj Pokrywka 2022-05-23 00:15:02 +02:00
parent 2b8884af80
commit 4768874dde
1 changed files with 22 additions and 22 deletions
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44
deepl.py
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@ -52,13 +52,13 @@ def train(epochs):
# data_dev = pd.read_csv('data_dev.csv', engine='python', header=None).dropna() # data_dev = pd.read_csv('data_dev.csv', engine='python', header=None).dropna()
# data_test = pd.read_csv('data_test.csv', engine='python', header=None).dropna() # data_test = pd.read_csv('data_test.csv', engine='python', header=None).dropna()
x_train = data_train[5] x_train = data_train["company_profile"]
x_dev = data_dev[5] x_dev = data_dev["company_profile"]
x_test = data_test[5] x_test = data_test["company_profile"]
y_train = data_train[17] y_train = data_train["fraudulent"]
y_dev = data_dev[17] y_dev = data_dev["fraudulent"]
y_test = data_test[17] y_test = data_test["fraudulent"]
company_profile = np.array(company_profile) company_profile = np.array(company_profile)
x_train = np.array(x_train) x_train = np.array(x_train)
@ -93,7 +93,7 @@ def train(epochs):
model = nn.Sequential( model = nn.Sequential(
nn.Linear(x_train.shape[1], 64), nn.Linear(x_train.shape[1], 64),
nn.ReLU(), nn.ReLU(),
nn.Linear(64, data_train[17].nunique()), nn.Linear(64, data_train["fraudulent"].nunique()),
nn.LogSoftmax(dim=1)) nn.LogSoftmax(dim=1))
# Define the loss # Define the loss
@ -148,7 +148,7 @@ def train(epochs):
log_ps = model(x_test) log_ps = model(x_test)
ps = torch.exp(log_ps) ps = torch.exp(log_ps)
top_p, top_class = ps.topk(1, dim=1) top_p, top_class = ps.topk(1, dim=1)
descr = np.array(data_test[5]) descr = np.array(data_test["fraudulent"])
for i, (x, y) in enumerate(zip(np.array(top_class), np.array(y_test.view(*top_class.shape)))): for i, (x, y) in enumerate(zip(np.array(top_class), np.array(y_test.view(*top_class.shape)))):
d = descr[i] d = descr[i]
if x == y: if x == y:
@ -171,22 +171,22 @@ def train(epochs):
f.write(f"FP = {len(FP)}\n") f.write(f"FP = {len(FP)}\n")
f.write(f"FN = {len(FN)}\n") f.write(f"FN = {len(FN)}\n")
f.write(f"TP descriptions:") # f.write(f"TP descriptions:")
for i in TP: # for i in TP:
f.write(i+'\n') # f.write(i+'\n')
f.write(f"TF descriptions:") # f.write(f"TF descriptions:")
for i in TF: # for i in TF:
f.write(i+"\n") # f.write(i+"\n")
f.write(f"FP descriptions:") # f.write(f"FP descriptions:")
for i in FP: # for i in FP:
f.write(i+"\n") # f.write(i+"\n")
f.write(f"FN descriptions:") # f.write(f"FN descriptions:")
for i in FN: # for i in FN:
f.write(i+"\n") # f.write(i+"\n")
f.close() # f.close()
torch.save(model, 'model') torch.save(model, 'model')
input_example = data_train[:5] # input_example = data_train[:5]
# siganture = infer_signature(input_example, np.array(['company_profile'])) # siganture = infer_signature(input_example, np.array(['company_profile']))
# path = urlparse(mlflow.get_tracking_uri()).scheme # path = urlparse(mlflow.get_tracking_uri()).scheme