Merge pull request 'fix repo link in readme' (#2) from 0.1.1 into master

Reviewed-on: #2

README.MD

@@ -8,7 +8,7 @@ Prices are valid only for transportation across the territory of the Russian Fed
 
 # 2. Installation
 
-    pip install git+https://gitea.dot-dot.ru/ilyuschenko/transit_calculator.git
+    pip install git+https://gitea.dot-dot.ru/dot-dot/transit_calculator.git
 
 # 3. Usage
 
@@ -16,19 +16,19 @@ Prices are valid only for transportation across the territory of the Russian Fed
 
 ```python
 from tcalc.model import TCalcPredictor
-from tcalc.utils import read_yaml
+from tcalc.config import TCalcDatasetConfig
 
 
-predictor = TCalcPredictor(dataset_config=read_yaml("config/dataset.yaml"))
+predictor = TCalcPredictor(dataset_config=TCalcDatasetConfig())
 
 # predictor can load models from links
 predictor.load_models("https://storage.yandexcloud.net/data-monsters/sber/actual_model.zip")
 
 # or from local folders (models will be loaded as ensemble)
-predictor.load_models("default_model")
+# predictor.load_models("default_model")
 
 # and files (single model)
-predictor.load_models("default_model/default_model.onnx")
+# predictor.load_models("default_model/default_model.onnx")
 
 ```
 

config/dataset.yaml

@@ -1,65 +0,0 @@
-start_date: "2023-01-01"
-date_format: "%Y-%m-%d"
-datetime_format: "%Y-%m-%d %H:%M:%S"
-
-input_columns:
-  - created_at
-  - pick_at
-  - from_lat
-  - from_lon
-  - to_lat
-  - to_lon
-  - weight
-  - volume
-
-preprocessing:
-  - name: from_lat
-    bias: 40
-    std: 4
-
-  - name: from_lon
-    bias: 40
-    std: 4
-
-  - name: to_lat
-    bias: 40
-    std: 4
-
-  - name: to_lon
-    bias: 40
-    std: 4
-
-  - name: distance
-    bias: 700
-    std: 950
-
-  - name: weight
-    bias: 12000
-    std: 8000
-
-  - name: volume
-    bias: 55
-    std: 31
-
-  - name: time_passed
-    bias: 6
-    std: 3
-
-  - name: car_type_id
-    bias: 1
-    std: 1
-
-  - name: urgency
-    bias: 0
-    std: 1
-
-distributions:
-  dot: [1, 0, 0]
-  trades: [0, 1, 0]
-  tariff: [0, 0, 1]
-
-use_distr: dot
-
-coordinates_thr:
-  lat: [39.6, 72.89]
-  lon: [19.32, 158.34]

fixtures/fixtures.py

@@ -2,16 +2,16 @@ import pytest
 
 from tcalc.dataset import TCalcDataset
 from tcalc.model import TCalcPredictor
-from tcalc.utils import read_yaml
+from tcalc.config import TCalcDatasetConfig
 
 
 @pytest.fixture(scope="session")
 def dataset():
-    return TCalcDataset(read_yaml("config/dataset.yaml"))
+    return TCalcDataset(TCalcDatasetConfig())
 
 
 @pytest.fixture(scope="session")
 def tcalc_predictor():
-    p = TCalcPredictor(read_yaml("config/dataset.yaml"))
+    p = TCalcPredictor(TCalcDatasetConfig())
     p.load_models("default_model")
     return p

requirements.txt

@@ -1,4 +1,3 @@
-pyyaml
 geopy>=2.4.1
 onnxruntime>=1.18.1
 requests>=2.32.3

setup.py

@@ -2,15 +2,14 @@ from setuptools import setup
 
 setup(
     name='tcalc',
-    version='0.1.0',    
+    version='0.1.1',    
     description='The model for determining the spot price of transportation from point A to point B (only for direct routes)',
     url='',
     author='Ilushenko Ivan',
     author_email='ilyuschenko@it.dot-dot.ru',
     license='',
     packages=['tcalc'],
-    install_requires=['pyyaml',
+    install_requires=['geopy>=2.4.1',   
-                      'geopy>=2.4.1',   
                       'onnxruntime>=1.18.1',
                       'requests>=2.32.3'  
                       ],

tcalc/__init__.py

@@ -1,2 +1,2 @@
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 __author__ = 'Ilushenko Ivan'

tcalc/config.py

@@ -0,0 +1,37 @@
+from dataclasses import dataclass, field
+from datetime import datetime
+
+
+@dataclass
+class TCalcDatasetConfig:
+    start_date: 'datetime' = datetime(year=2023, month=1, day=1)
+    date_format: str = "%Y-%m-%d"
+    datetime_format: str = "%Y-%m-%d %H:%M:%S"
+
+    input_columns: list = field(default_factory=lambda: [
+        "created_at", "pick_at", "from_lat", "from_lon",
+        "to_lat", "to_lon", "weight", "volume"
+    ])
+
+    preprocessing: list[dict] = field(default_factory=lambda: [
+        {"name": "from_lat", "bias": 40, "std": 4},
+        {"name": "from_lon", "bias": 40, "std": 4},
+        {"name": "to_lat", "bias": 40, "std": 4},
+        {"name": "to_lon", "bias": 40, "std": 4},
+        {"name": "distance", "bias": 700, "std": 950},
+        {"name": "weight", "bias": 12000, "std": 8000},
+        {"name": "volume", "bias": 55, "std": 31},
+        {"name": "time_passed", "bias": 6, "std": 3},
+        {"name": "car_type_id", "bias": 1, "std": 1},
+        {"name": "urgency", "bias": 0, "std": 1},
+    ])
+    distributions: dict = field(default_factory=lambda: {
+        "dot": [1, 0, 0],
+        "trades": [0, 1, 0],
+        "tariff": [0, 0, 1]
+    })
+    use_distr: str = "dot"
+    coordinates_thr: dict = field(default_factory=lambda: {
+        "lat": [39.6, 72.89],
+        "lon": [19.32, 158.34]
+    })

tcalc/dataset.py

@@ -1,19 +1,18 @@
 from datetime import datetime
-from copy import deepcopy
 
 import numpy as np
 
 from tcalc.utils import direct_distance
+from tcalc.config import TCalcDatasetConfig
 
 
 class TCalcDataset:
-    def __init__(self, config: dict) -> None:
+    def __init__(self, config: TCalcDatasetConfig) -> None:
         """
         Args:
-            config (str): Dataset config.
+            config (TCalcDatasetConfig): Dataset config.
         """
-        self.config = deepcopy(config)
+        self.config = config
-        self.start_date = datetime.strptime(config["start_date"], self.config["date_format"])
         self.data = np.array([])
 
     def __len__(self) -> int:
@@ -40,7 +39,7 @@ class TCalcDataset:
         Raises:
             ValueError: Missing columns.
         """
-        requirement_cols = set(self.config["input_columns"])
+        requirement_cols = set(self.config.input_columns)
 
         for i, row in enumerate(data):
             cols = requirement_cols - set(row.keys())
@@ -73,14 +72,14 @@ class TCalcDataset:
         """
         row = row.copy()
 
-        row["created_at"] = datetime.strptime(row["created_at"], self.config["datetime_format"])
+        row["created_at"] = datetime.strptime(row["created_at"], self.config.datetime_format)
-        if row["created_at"] < self.start_date:
+        if row["created_at"] < self.config.start_date:
-            raise ValueError(f"'created_at' field cant be more than {self.start_date}, {row['created_at']} passed.")
+            raise ValueError(f"'created_at' field cant be more than {self.config.start_date}, {row['created_at']} passed.")
 
-        time_passed = row["created_at"] - self.start_date
+        time_passed = row["created_at"] - self.config.start_date
         row["time_passed"] = round(time_passed.total_seconds() / (60 * 60 * 24 * 30), 1)
         
-        row["pick_at"] = datetime.strptime(row["pick_at"], self.config["datetime_format"])
+        row["pick_at"] = datetime.strptime(row["pick_at"], self.config.datetime_format)
         if row["pick_at"] < row["created_at"]:
             raise ValueError(f"'pick_at' field cant be more than 'created_at' field, {row['pick_at']} and {row['created_at']} passed.")
 
@@ -88,7 +87,7 @@ class TCalcDataset:
         row["urgency"] = int(row["urgency"] < 48)
         
         if not self._check_coordinates(row):
-            raise ValueError(f"Coordinates must be in range {self.config['coordinates_thr']}")
+            raise ValueError(f"Coordinates must be in range {self.config.coordinates_thr}")
         
         row["distance"] = direct_distance(row)
 
@@ -99,11 +98,11 @@ class TCalcDataset:
             row["car_type_id"] = 1
 
         output = []
-        for elem in self.config["preprocessing"]:
+        for elem in self.config.preprocessing:
             val = (row[elem["name"]] - elem["bias"]) / elem["std"]
             output.append(val)
         
-        output += self.config["distributions"][self.config["use_distr"]]
+        output += self.config.distributions[self.config.use_distr]
         output.append(row["distance"])
         return output
     
@@ -116,7 +115,7 @@ class TCalcDataset:
         Returns:
             bool: Coordinates is ok.
         """
-        conf = self.config["coordinates_thr"]
+        conf = self.config.coordinates_thr
 
         for col in ["from_lat", "to_lat"]:
             if row[col] < conf["lat"][0] or row[col] > conf["lat"][1]:

tcalc/model.py

@@ -8,6 +8,7 @@ import onnxruntime
 import requests
 
 from tcalc.dataset import TCalcDataset
+from tcalc.config import TCalcDatasetConfig
 
 
 def convert_input(inp: np.ndarray, names: list[str]) -> dict:
@@ -57,12 +58,12 @@ class TCalcModel:
 
 
 class TCalcPredictor:
-    def __init__(self, dataset_config: dict) -> None:
+    def __init__(self, dataset_config: TCalcDatasetConfig) -> None:
         """
         Args:
-            dataset_config (dict): TCalcDataset config.
+            dataset_config (TCalcDatasetConfig): TCalcDataset config.
         """
-        self.dataset_config = deepcopy(dataset_config)
+        self.dataset_config = dataset_config
 
     def load_models(self, path: str) -> None:
         """Load models from directory, .onnx file or link with zip archive.

tcalc/utils.py

@@ -1,25 +1,6 @@
-from typing import Union
-from pathlib import Path
-
-import yaml
 import geopy.distance
 
 
-def read_yaml(path: Union[str, Path], encoding: str = "utf-8") -> dict:
-    """Read content of yaml file.
-
-    Args:
-        path (Union[str, Path]): Path to file.
-        encoding (str, optional): Defaults to "utf-8".
-
-    Returns:
-        dict: File content.
-    """
-    with open(path, "r", encoding=encoding) as f:
-        result = yaml.safe_load(f)
-        return result
-    
-
 def direct_distance(row: dict) -> float:
     """Direct distance between two points.
 

tests/test_model.py

@@ -1,15 +1,15 @@
 from fixtures.fixtures import *
 from tcalc.model import TCalcPredictor
-from tcalc.utils import read_yaml
+from tcalc.config import TCalcDatasetConfig
 
 
 def test_predictor_from_file():
-    p = TCalcPredictor(read_yaml("config/dataset.yaml"))
+    p = TCalcPredictor(TCalcDatasetConfig())
     p.load_models("default_model/default_model.onnx")
 
 
 def test_predictor_from_folder():
-    p = TCalcPredictor(read_yaml("config/dataset.yaml"))
+    p = TCalcPredictor(TCalcDatasetConfig())
     p.load_models("default_model")