pabloalcain

...
save_model(classifier, "model.pkl")
...

pabloalcain

import pickle

def load_model(filename):
    with open(filename, "rb") as model_file:
        classifier = pickle.load(model_file)
    return classifier

def save_model(classifier, filename):
    with open(filename, "wb") as model_file:
        pickle.dump(classifier, model_file)

pabloalcain

import...

dataset = pd.read_csv("unseen_iris.csv")
features = dataset[["s-l", "s-w", "p-l", "p-w"]]
target = dataset["variety"]

with open("model.pkl", "rb") as model_file:
    classifier = pickle.load(model_file)

score = accuracy_score(target, classifier.predict(features))

pabloalcain

import...

dataset = pd.read_csv("iris.csv")
features = dataset[["s-l", "s-w", "p-l", "p-w"]]
target = dataset["variety"]

classifier = Pipeline(steps=[("select", SelectKBest(k=2)),
                             ("clf", LogisticRegression())])
classifier.fit(features, target)
with open("model.pkl", "wb") as model_file:

pabloalcain

import pandas as pd
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline

dataset = pd.read_csv("iris.csv")
features = dataset[["s-l", "s-w", "p-l", "p-w"]]
target = dataset["variety"]

pabloalcain

import typing

from pyspark.sql import DataFrame

T = typing.TypeVar("T", bound="DynamicDataFrame")


class DynamicDataFrame(DataFrame):
    def __init__(self, df: DataFrame):
        super().__init__(df._jdf, df.sql_ctx)

pabloalcain

import pyrealsense2 as rs

pipeline = rs.pipeline()
profile = pipeline.start()
align = rs.align(rs.stream.color)

all_color_frames = []
all_depth_frames = []
for i in range(20):
  print(i)