I am trying to run a kNN (k-nearest neighbour) algorithm in Python.
The dataset I am using to try and do this is available at the UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/wine
Here is the code I am using:
#1. LIBRARIES
import os
import pandas as pd
import numpy as np
print os.getcwd() # Prints the working directory
os.chdir('C:\\file_path') # Provide the path here
#2. VARIABLES
variables = pd.read_csv('wines.csv')
winery = variables['winery']
alcohol = variables['alcohol']
malic = variables['malic']
ash = variables['ash']
ash_alcalinity = variables['ash_alcalinity']
magnesium = variables['magnesium']
phenols = variables['phenols']
flavanoids = variables['flavanoids']
nonflavanoids = variables['nonflavanoids']
proanthocyanins = variables['proanthocyanins']
color_intensity = variables['color_intensity']
hue = variables['hue']
od280 = variables['od280']
proline = variables['proline']
#3. MAX-MIN NORMALIZATION
alcoholscaled=(alcohol-min(alcohol))/(max(alcohol)-min(alcohol))
malicscaled=(malic-min(malic))/(max(malic)-min(malic))
ashscaled=(ash-min(ash))/(max(ash)-min(ash))
ash_alcalinity_scaled=(ash_alcalinity-min(ash_alcalinity))/(max(ash_alcalinity)-min(ash_alcalinity))
magnesiumscaled=(magnesium-min(magnesium))/(max(magnesium)-min(magnesium))
phenolsscaled=(phenols-min(phenols))/(max(phenols)-min(phenols))
flavanoidsscaled=(flavanoids-min(flavanoids))/(max(flavanoids)-min(flavanoids))
nonflavanoidsscaled=(nonflavanoids-min(nonflavanoids))/(max(nonflavanoids)-min(nonflavanoids))
proanthocyaninsscaled=(proanthocyanins-min(proanthocyanins))/(max(proanthocyanins)-min(proanthocyanins))
color_intensity_scaled=(color_intensity-min(color_intensity))/(max(color_intensity)-min(color_intensity))
huescaled=(hue-min(hue))/(max(hue)-min(hue))
od280scaled=(od280-min(od280))/(max(od280)-min(od280))
prolinescaled=(proline-min(proline))/(max(proline)-min(proline))
alcoholscaled.mean()
alcoholscaled.median()
alcoholscaled.min()
alcoholscaled.max()
#4. DATA FRAME
d = {'alcoholscaled' : pd.Series([alcoholscaled]),
'malicscaled' : pd.Series([malicscaled]),
'ashscaled' : pd.Series([ashscaled]),
'ash_alcalinity_scaled' : pd.Series([ash_alcalinity_scaled]),
'magnesiumscaled' : pd.Series([magnesiumscaled]),
'phenolsscaled' : pd.Series([phenolsscaled]),
'flavanoidsscaled' : pd.Series([flavanoidsscaled]),
'nonflavanoidsscaled' : pd.Series([nonflavanoidsscaled]),
'proanthocyaninsscaled' : pd.Series([proanthocyaninsscaled]),
'color_intensity_scaled' : pd.Series([color_intensity_scaled]),
'hue_scaled' : pd.Series([huescaled]),
'od280scaled' : pd.Series([od280scaled]),
'prolinescaled' : pd.Series([prolinescaled])}
df = pd.DataFrame(d)
#5. TRAIN-TEST SPLIT
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(np.matrix(df),np.matrix(winery),test_size=0.3)
print X_train.shape, y_train.shape
print X_test.shape, y_test.shape
#6. K-NEAREST NEIGHBOUR ALGORITHM
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(X_train, y_train)
print("Test set score: {:.2f}".format(knn.score(X_test, y_test)))
In section 5, when I run sklearn.model_selection to import the train-test split mechanism, this does not appear to be running correctly because it provides the shapes: (0,13) (0,178) (1,13) (1,178).
Then, upon trying to run the knn, I get the error message: Found array with 0 sample(s) (shape=(0,13)) while a minimum of 1 is required. This is not due to scaling with max-min normalisation as I still get this error message even when the variables are not scaled.
I'm not exactly sure where your code is going wrong, it's a slightly different way of going about it compared to the sklearn docs. However, I can show you a different way of getting the train test split to work on the wine dataset for you.
from sklearn.datasets import load_wine
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
X, y = load_wine(return_X_y=True)
X_scaled = MinMaxScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y,
test_size=0.3)
knn = KNeighborsClassifier(n_neighbors=10)
knn.fit(X_train, y_train)
Related
I'm trying to apply ML on atomic structures using descriptors. My problem is that I get very different score values depending on the datasize I use, I suspect that something is wrong with my model, any suggestions would be appreciated. I used dataset from this paper (Dataset MoS2(single)).
Here is the my code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import ase
from dscribe.descriptors import SOAP
from dscribe.descriptors import CoulombMatrix
from sklearn.model_selection import train_test_split
import sklearn
from sklearn.linear_model import LinearRegression
from sklearn.kernel_ridge import KernelRidge
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVR
from ase.io import read
materials = read('structures.xyz', index=':')
materials = materials[:5000]
energies = pd.read_csv('Energy.csv')
energies = np.array(energies['b'])
energies = energies[:5000]
species = ["H", 'Mo', 'S']
rcut = 8.0
nmax = 1
lmax = 1
# Setting up the SOAP descriptor
soap = SOAP(
species=species,
periodic=False,
rcut=rcut,
nmax=nmax,
lmax=lmax,
)
coulomb_matrices = soap.create(materials, positions=[[51]]*len(materials))
nsamples, nx, ny = coulomb_matrices.shape
d2_train_dataset = coulomb_matrices.reshape((nsamples,nx*ny))
df = pd.DataFrame(d2_train_dataset)
df['target'] = energies
from sklearn.preprocessing import StandardScaler
X = df.iloc[:, 0:12].values
y = df.iloc[:, 12:].values
st_x = StandardScaler()
st_y = StandardScaler()
X = st_x.fit_transform(X)
y = st_y.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y)
#krr = GridSearchCV(
# KernelRidge(kernel="rbf", gamma=0.1),
# param_grid={"alpha": [1e0, 0.1, 1e-2, 1e-3], "gamma": np.logspace(-2, 2, 5)},
#)
svr = GridSearchCV(
SVR(kernel="rbf", gamma=0.1),
param_grid={"C": [1e0, 1e1, 1e2, 1e3], "gamma": np.logspace(-2, 2, 5)},
)
svr = svr.fit(X_train, y_train.ravel())
print("Training set score: {:.4f}".format(svr.score(X_train, y_train)))
print("Test set score: {:.4f}".format(svr.score(X_test, y_test)))
and score:
Training set score: 0.0414
Test set score: 0.9126
I don't have a full answer to your problem as recreating it would be very cumbersome, but here are some questions to check:
a) You are training on 5 CrossValidation folds (default). First you should check the results of all parameter combinations right after the fitting process with "svr.best_score_" (or more detailed with "svr.cv_results_dict") and see what mean score your folds actually produced. If the score is really is as low as 0.04 (I assume higher is better, which these scores usually do), taking the reciprocal prediction would actually be really accurate! If you know you're always wrong, it's really easy to be right. ;D
b) You could go ahead and just use the svr.best_params_ in order to train again on the whole X_train-set instead of the folds (this can also be achieved with the "refit"-option of RandomSearchCV as well) and then check with the test set again. Here could also be the actual error: The documentation for the score method of GridSearchCV reads: "Return the score on the given data, if the estimator has been refit." This is not the case in your gridsearch! Try turning the refit option on. Maybe that works? ... sorry, your code was too cumbersome to be replicated fast, so I didn't check myself ...
i just recently started learning data science. this is what i wrote:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.metrics import precision_score, recall_score
import numpy as np
#reading data
df = pd.read_csv('titanic.csv')
df['male'] = df['Sex'] == 'male'
X = df[['Pclass', 'male', 'Age', 'Siblings/Spouses', 'Parents/Children', 'Fare']].values
y = df['Survived'].values
#spliting data into train/test
kf = KFold(n_splits=4+1, shuffle=True, random_state=10)
tree_scores = {'accuracy_scores':[],'precision_scores':[],'recall_scores':[]}
logistic_scores = {'accuracy_scores':[],'precision_scores':[],'recall_scores':[]}
#making the models
for train_indexes, test_indexes in kf.split(X):
X_train, X_test = X[train_indexes], X[test_indexes]
y_train, y_test = y[train_indexes], y[test_indexes]
tree = DecisionTreeClassifier()
tree.fit(X_train, y_train)
tree_scores['accuracy_scores'].append(tree.score(X_test,y_test))
tree_prediction = tree.predict(X_test)
#tree_scores['precision_scores'].append(tree.precision_score(y_test,tree_prediction))
#tree_scores['recall_scores'].append(tree.recall_score(y_test,tree_prediction))
logistic = LogisticRegression()
logistic.fit(X_train,y_train)
logistic_scores['accuracy_scores'].append(logistic.score(X_test,y_test))
logistic_prediction = logistic.predict(X_test)
logistic_scores['precision_scores'].append(precision_score(y_test,logistic_prediction))
logistic_scores['recall_scores'].append(recall_score(y_test,logistic_prediction))
print("Decision Tree")
print(" accuracy:", np.mean(tree_scores['accuracy_scores']))
print(" precision:", np.mean(tree_scores['precision_scores']))
print(" recall:", np.mean(tree_scores['recall_scores']))
print("Logistic Regression")
print(" accuracy:", np.mean(logistic_scores['accuracy_scores']))
print(" precision:", np.mean(logistic_scores['precision_scores']))
print(" recall:", np.mean(logistic_scores['recall_scores']))
the two lines commented in for loop give error for both precision and recall, i dont know why. ALthough before when i was running both precision n recall they worked. and i cant seem to find any spelling mistake either.
i wonder if the different python syntaxes are messing with sklearn? because once i tried a combination like this:
X = df.loc['Plass':'Fare'].values
y = df.Survived.values
and it gave errors but when i used normal expected way it worked fine.
(note: the code may be wrongly indented, first time using stackexchange guys.)
DecisionTreeClassifier doesn't have such a method indeed.
You need to change:
tree_scores['precision_scores'].append(tree.precision_score(y_test,tree_prediction))
tree_scores['recall_scores'].append(tree.recall_score(y_test,tree_prediction))
to:
tree_scores['precision_scores'].append(precision_score(y_test,tree_prediction))
tree_scores['recall_scores'].append(recall_score(y_test,tree_prediction))
and you're fine to go
I am trying to find the relative similarity, and/or difference, between items, such as basketball players. I am using a KNN classifier for this task. For instance, based on data, I want to see how similar Lebron James is to, let's say Carmelo Anthony, and I want to see how similar Lebron James is to Ray Allen, and I want to see how similar Carmelo Anthony is to Ray Allen. I want to compare each person to each other person.
I am running the code below.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
with open('C:\\path_here\\nba.csv', 'r') as csvfile:
dataset = pd.read_csv(csvfile)
print(dataset.columns.values)
# convert to dataframe
dataset=pd.DataFrame(dataset)
dataset.dtypes
# fill NAs with zeros
dataset = dataset.fillna(0)
dataset.isnull().sum()
dataset.isnull().sum().sum()
dataset.head()
X = dataset.iloc[:,4:27]
y = dataset.iloc[:,28]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors=5)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
The data comes from here:
https://www.dropbox.com/s/b3nv38jjo5dxcl6/nba_2013.csv?dl=0
Basically, the code runs fine, but the output looks weird, or I forgot to include something. Anyway, I’m trying to get results something like this:
LebronJames vs. CarmeloAnthony: .95
CarmeloAnthony vs. RayAllen: .92
RayAllen vs. LebronJames: .91
https://pythonhosted.org/scikit-fuzzy/auto_examples/plot_cmeans.html
Fuzzy Group By, Grouping Similar Words
I am tring to use Random Forest Classifier from scikit learn in Python to predict stock movements. My dataset has 8 features and 1201 records. But after fitting the model and using it to predict, it appears 100% of accuracy and 100% of OOB error. I modified the n_estimators from 100 to a small value, but the OOB error has just dropped few %. Here is my code:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
#File reading
df = pd.read_csv('700.csv')
df.drop(df.columns[0],1,inplace=True)
target = df.iloc[:,8]
print(target)
#train test split
X_train, X_test, y_train, y_test = train_test_split(df, target, test_size=0.3)
#model fit
clf = RandomForestClassifier(n_estimators=100, criterion='gini',oob_score= True)
clf.fit(X_train,y_train)
pred = clf.predict(X_test)
accuaracy = accuracy_score(y_test,pred)
print(clf.oob_score_)
print(accuaracy)
How can I modifiy the code in order to make the oob error drop? Thanks.
If you want to check the error then use/modify your code like this one :
oob_error = 1 - clf.oob_score_
I am totally new to machine learning, I am currently playing with MNIST machine learning, using RandomForestClassifier.
I use sklearn and panda.
I have a training CSV data set.
import pandas as pd
import numpy as np
from sklearn import model_selection
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
train = pd.read_csv("train.csv")
features = train.columns[1:]
X = train[features]
y = train['label']
user_train = pd.read_csv("input.csv")
user_features = user_train.columns[1:]
y_train = user_train[user_features]
user_y = user_train['label']
X_train, X_test, y_train, y_test = model_selection.train_test_split(X/255.,y,test_size=1,random_state=0)
clf_rf = RandomForestClassifier()
clf_rf.fit(X_train, y_train)
y_pred_rf = clf_rf.predict(X_test)
acc_rf = accuracy_score(y_test, y_pred_rf)
print("pred : ", y_pred_rf)
print("random forest accuracy: ",acc_rf)
I have the current code, which works well. It takes the training set, split and take one element for testing, and does the prediction.
What I want now is to use the testing data from an input, I have a new csv called "input.csv", and I want to predict the value inside this csv.
How can I replace the model_selection.train_test_split with my input data ?
I am sure the response is very obvious, and I didn't find anything.
The following part of your code is unused
user_train = pd.read_csv("input.csv")
user_features = user_train.columns[1:]
y_train = user_train[user_features]
user_y = user_train['label']
If input.csv has the same structure of train.csv you may want to:
train a classifier and test it on a split of the input.csv dataset: (please refer to http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html to know how to set the test size)
input_train = pd.read_csv("input.csv")
input_features = user_train.columns[1:]
input_data = user_train[input_features]
input_labels = user_train['label']
data_train, data_test, labels_train, labels_test = model_selection.train_test_split(input_data/255.,input_labels,test_size=1,random_state=0)
clf_rf = RandomForestClassifier()
clf_rf.fit(data_train, labels_train)
labels_pred_rf = clf_rf.predict(data_test)
acc_rf = accuracy_score(labels_test, labels_pred_rf)
test the previously trained classifier on the whole input.csv file
input_train = pd.read_csv("input.csv")
input_features = user_train.columns[1:]
input_data = user_train[input_features]
input_labels = user_train['label']
labels_pred_rf = clf_rf.predict(input_data)
acc_rf = accuracy_score(input_labels, labels_pred_rf)