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p08-data-fixed.py
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p08-data-fixed.py
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from sklearn.model_selection import train_test_split
from sklearn.feature_extraction import DictVectorizer
import numpy as np
from scipy.spatial.distance import euclidean
from typing import List, Tuple
from tqdm import tqdm
import csv
from shared import dataset_local_path
ys = []
examples = []
with open(dataset_local_path("AirQualityUCI.csv")) as fp:
# This is a CSV file where the separators are not commas!
rows = csv.reader(fp, delimiter=";")
header = next(rows)
for row in rows:
datapoint = {}
# {'Date': '10/03/2004', 'Time': '18.00.00',
# 'CO(GT)': '2,6', 'PT08.S1(CO)': '1360', 'NMHC(GT)': '150', 'C6H6(GT)': '11,9',
# 'PT08.S2(NMHC)': '1046', 'NOx(GT)': '166', 'PT08.S3(NOx)': '1056',
# 'NO2(GT)': '113', 'PT08.S4(NO2)': '1692', 'PT08.S5(O3)': '1268',
# 'T': '13,6', 'RH': '48,9', 'AH': '0,7578', '': ''}
date = None
time = None
for (column_name, column_value) in zip(header, row):
if column_value == "" or column_name == "":
continue
elif column_name == "Date":
date = column_value
elif column_name == "Time":
time = column_value
else:
as_float = float(column_value.replace(",", "."))
if as_float == -200:
continue
datapoint[column_name] = as_float
if not datapoint:
continue
if "CO(GT)" not in datapoint:
continue
target = datapoint["CO(GT)"]
del datapoint["CO(GT)"]
ys.append(target)
examples.append(datapoint)
#%% Split data: (note 90% of 90% to make vali/test smaller)
RANDOM_SEED = 1234
## split off train/validate (tv) pieces.
ex_tv, ex_test, y_tv, y_test = train_test_split(
examples,
ys,
train_size=0.9,
shuffle=True,
random_state=RANDOM_SEED,
)
# split off train, validate from (tv) pieces.
ex_train, ex_vali, y_train, y_vali = train_test_split(
ex_tv, y_tv, train_size=0.9, shuffle=True, random_state=RANDOM_SEED
)
#%% vectorize:
from sklearn.preprocessing import StandardScaler, MinMaxScaler
feature_numbering = DictVectorizer(sparse=False)
# Learn columns from training data (again)
feature_numbering.fit(ex_train)
rX_train = feature_numbering.transform(ex_train)
rX_vali = feature_numbering.transform(ex_vali)
rX_test = feature_numbering.transform(ex_test)
scaling = StandardScaler()
X_train = scaling.fit_transform(rX_train)
X_vali = scaling.transform(rX_vali)
X_test = scaling.transform(rX_test)
print(X_train.shape, X_vali.shape)
#%% train a model:
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import SGDRegressor
from sklearn.neural_network import MLPRegressor
from sklearn.neighbors import KNeighborsRegressor
m = KNeighborsRegressor(n_neighbors=5, weights="distance")
m.fit(X_train, y_train)
print(m.score(X_vali, y_vali))
## Lab TODO:
# Mandatory:
# - Try some other regression models.
# Options:
# - Try all the other regression models.
# - Research the AirQualityUCI dataset to see what the best approaches are!
# - Try at least one, plot a (y_pred, y_actual) scatter plot (e.g., visualize correlation / R**2)
# - [Difficult] see the brute-force kNN below, try to refactor the loops out of python.
# %% kNN Brute Force Below:
# Note, this is really slow (see progress bar!)
def knn_regress(
X_train: np.ndarray, y_train: np.ndarray, x: np.ndarray, k: int = 3
) -> float:
(num_examples, num_features) = X_train.shape
assert num_examples == len(y_train)
assert len(x) == num_features
assert k < num_examples
# fill in list of distances to training labels:
# (distance, y_value)
# This should be a heap, not a list, but python's heapq is annoying.
scored_examples: List[Tuple[float, float]] = []
for (i, row) in enumerate(X_train):
distance = euclidean(row, x)
scored_examples.append((distance, y_train[i]))
# find closest-k:
sum_y = 0.0
for (_distance, close_y) in sorted(scored_examples)[:k]:
sum_y += close_y
return sum_y / k
do_slow = False
if do_slow:
# Loop over each element of validation set, and predict based on training.
y_vali_pred = []
for row_index in tqdm(range(len(y_vali)), desc="kNN Brute Force"):
example = X_vali[row_index, :]
y_vali_pred.append(knn_regress(X_train, y_train, example, k=3))
from sklearn.metrics import r2_score
print("Manual KNN:", r2_score(y_vali, y_vali_pred))
## TODO (optional, Challenging!) (efficiency / matrix ops)
#
# Converting our Manual KNN to use scipy.spatial.distance.cdist
# *should* allow it to compute a matrix of distances between
# X_train and X_vali as 1 call to the scipy C/Fortran library.
#
# ... This may be significantly faster.
# ... You'll then end up here or so: https://stackoverflow.com/questions/6910641/how-do-i-get-indices-of-n-maximum-values-in-a-numpy-array
# ... Seriously, I find doing this stuff annoying.
# ... Good luck!