UFuncTypeError：ufunc 'matmul' 不包含具有签名匹配类型的循环（dtype（' <u32'), dtype('<u32')) -> dtype(' <u32') - streamlit< div><div id="text_translate"><pre> #Linear Regression Model @st.cache(allow_output_mutation=True) def linearRegression(X_train, X_test, y_train, y_test): model = LinearRegression() model.fit(X_train,y_train) score = model.score(X_test, y_test)*100 return score, model</pre><hr><pre> #User input for the model def user_input(): bedrooms = st.slider("Bedrooms: ", 1,15) bathrooms = st.text_input("Bathrooms: ") sqft_living = st.text_input("Square Feet: ") sqft_lot = st.text_input("Lot Size: ") floors = st.text_input("Number Of Floors: ") waterfront = st.text_input("Waterfront? For Yes type '1', For No type '0': ") view = st.slider("View (A higher score will mean a better view): ", 0,4) condition = st.slider("House Condition (A higher score will mean a better condition): ", 1,5) yr_built = st.text_input("Year Built: ") yr_reno = st.text_input("A Renovated Property? For Yes type '1', For No type '0': ") zipcode = st.text_input("Zipcode (5 digit): ") year_sold = st.text_input("Year Sold: ") month_sold = st.slider("Month Sold: ", 1,12) user_input_prediction = np.array([bedrooms,bathrooms,sqft_living, sqft_lot,floors,waterfront,view,condition,yr_built,yr_reno,zipcode,year_sold,month_sold]).reshape(1,-1) return(user_input_prediction)</pre><hr><pre> #Main function if(st.checkbox("Start a Search")): user_input_prediction = user_input() st.write('error1') pred = model.predict(user_input_prediction) st.write('error2') if(st.button("Submit")): st.text("success")</pre><p> 我正在使用 Streamlit 构建一个接受用户输入的 ML model。 在我的主要 function 中，它返回错误UFuncTypeError: ufunc 'matmul' did not contain a loop with signature matching types (dtype('&lt;U32'), dtype('&lt;U32')) -&gt; dtype('&lt;U32') and trace返回pred = model.predict(user_input_prediction)主 function 将打印出 error1 但不会打印 error2</p></div></u32')></u32'),>

Question

#Linear Regression Model 
@st.cache(allow_output_mutation=True)
def linearRegression(X_train, X_test, y_train, y_test):
    model = LinearRegression()
    model.fit(X_train,y_train)
    score = model.score(X_test, y_test)*100

    return score , model      

---

#User input for the model
def user_input():
    bedrooms = st.slider("Bedrooms: ", 1,15)
    bathrooms = st.text_input("Bathrooms: ")
    sqft_living = st.text_input("Square Feet: ")
    sqft_lot = st.text_input("Lot Size: ")
    floors = st.text_input("Number Of Floors: ")
    waterfront = st.text_input("Waterfront? For Yes type '1',  For No type '0': ")
    view = st.slider("View (A higher score will mean a better view) : ", 0,4)
    condition = st.slider("House Condition (A higher score will mean a better condition): ", 1,5)
    yr_built = st.text_input("Year Built: ")
    yr_reno = st.text_input("A Renovated Property? For Yes type '1',  For No type '0': ")
    zipcode = st.text_input("Zipcode (5 digit): ")
    year_sold = st.text_input("Year Sold: ")
    month_sold = st.slider("Month Sold: ", 1,12)
   
    user_input_prediction = np.array([bedrooms,bathrooms,sqft_living, sqft_lot,floors,waterfront,view,condition,yr_built,yr_reno,zipcode,year_sold,month_sold]).reshape(1,-1)
    
    return(user_input_prediction)

---

#Main function


            if(st.checkbox("Start a Search")):
                user_input_prediction = user_input()
                st.write('error1')
                pred = model.predict(user_input_prediction)
                st.write('error2')
                if(st.button("Submit")):
                    st.text("success")
                    
                 

I am using Streamlit to build a ML model that take user input. In my main function it returns error UFuncTypeError: ufunc 'matmul' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32') and trace back to pred = model.predict(user_input_prediction) the main function will print out error1 but not error2

Answer 1

I was stuck in same kind of situation where the model was throwing various kinds of errors. But particularly for your case, let me tell what I tried:

This line of yours seems pretty well.

user_input_prediction = np.array([bedrooms,bathrooms,sqft_living, sqft_lot,floors,waterfront,view,condition,yr_built,yr_reno,zipcode,year_sold,month_sold]).reshape(1,-1)

Just try adding this line below your code

user_input_prediction = user_input_prediction.astype(np.float64)

Because here model was throwing error that your datatype is mismatch since inside all these values of features are in form of a matrix(numeric) so we need to convert it into floating values before doing any prediction.

Also try passing the user_input_prediction inside predict method as a list:

preds = model.predict([user_input_prediction])

This worked for me, hope it'll work for you as well