solution

.gitignore

@@ -0,0 +1,2 @@
+*~
+*.pyc

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,12 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPep8NamingInspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <option name="ignoredErrors">
+        <list>
+          <option value="N802" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/mieszkania5.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/mieszkania5.iml" filepath="$PROJECT_DIR$/.idea/mieszkania5.iml" />
+    </modules>
+  </component>
+</project>

README.md

@@ -0,0 +1,6 @@
+Mieszkania5 challenge
+=====================
+
+Guess the price of a flat/house.
+
+The metric is absolute mean error.

config.txt

@@ -0,0 +1 @@
+--metric MAE --precision 1

dev-0/expected.tsv

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mieszkania5.py

@@ -0,0 +1,77 @@
+import torch
+import torch.optim as op
+from torch.autograd import Variable
+from price_parser import Price
+
+train = open('train/train.tsv', 'r')
+test_in = open('test-A/in.tsv', 'r')
+test_out = open('test-A/out.tsv', 'w+')
+
+expected = []
+prices = []
+
+
+def count_loss(y_predicted, y_target):
+    return ((y_predicted - y_target) ** 2).sum()
+
+
+def read_in_file_as_line():
+    with test_in as tin:
+        lines = tin.readlines()
+    return lines
+
+
+def count(a, b, x):
+    return a * x + b
+
+
+def optimization(a, b, x, y):
+    optimization = op.Adam([a, b], lr=0.02)
+
+    for t in range(1000):
+        optimization.zero_grad()
+        y_predicted = count(a, b, x)
+        current_loss = count_loss(y_predicted, y)
+        current_loss.backward()
+        optimization.step()
+
+
+def create_prize_and_exp_tab():
+    for line in train.readlines():
+        price = Price.fromstring(line.split('\t', 1)[1])
+        if price.currency == "zł" and price.currency == "PLN":
+            a = line.split('\t', 1)[0]
+            float_price = price.amount_float
+            while int(float_price) < 100000:
+                float_price = float_price * 10
+            expected.append(float(a))
+            prices.append(float_price)
+
+    x = torch.tensor(prices, dtype=torch.float)
+    y = torch.tensor(expected, dtype=torch.float)
+    a = torch.randn(1, requires_grad=True)
+    b = torch.randn(1, requires_grad=True)
+
+    optimization(a, b, x, y)
+
+    with test_out as out:
+        for line in read_in_file_as_line():
+            i = 0
+            text = line.split('\t')
+            price = Price.fromstring(text[i])
+            while price.currency != "zł" and price.currency != "PLN" and i < len(text):
+                price = Price.fromstring(text[i])
+                i += 1
+            if (price.currency != "zł" and price.currency != "PLN") or price.amount is None or price.amount == 0:
+                price = Price.fromstring("350100")
+            float_price = price.amount_float
+            while int(float_price) < 100000:
+                float_price = float_price * 10
+            predict = Variable(torch.Tensor([float_price]))
+            predicted_price = count(a, b, predict).detach()
+            out.write(str(predicted_price.item()))
+            out.write('\n')
+
+
+if __name__ == '__main__':
+    create_prize_and_exp_tab()